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Relevant bibliographies by topics / Hill-climbing optimization

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Academic literature on the topic 'Hill-climbing optimization'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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Journal articles on the topic "Hill-climbing optimization"

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Abualigah, Laith Mohammad, Essam Said Hanandeh, Ahamad Tajudin Khader, Mohammed Abdallh Otair, and Shishir Kumar Shandilya. "An Improved B-hill Climbing Optimization Technique for Solving the Text Documents Clustering Problem." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 4 (May 7, 2020): 296–306. http://dx.doi.org/10.2174/1573405614666180903112541.

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Background: Considering the increasing volume of text document information on Internet pages, dealing with such a tremendous amount of knowledge becomes totally complex due to its large size. Text clustering is a common optimization problem used to manage a large amount of text information into a subset of comparable and coherent clusters. Aims: This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the problem of the text document clustering through modeling the β-hill climbing technique for partitioning the similar documents into the same cluster. Methods: The β parameter is the primary innovation in β-hill climbing technique. It has been introduced in order to perform a balance between local and global search. Local search methods are successfully applied to solve the problem of the text document clustering such as; k-medoid and kmean techniques. Results: Experiments were conducted on eight benchmark standard text datasets with different characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed β-hill climbing achieved better results in comparison with the original hill climbing technique in solving the text clustering problem. Conclusion: The performance of the text clustering is useful by adding the β operator to the hill climbing.

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Khari, Manju, and Prabhat Kumar. "Empirical Evaluation of Hill Climbing Algorithm." International Journal of Applied Metaheuristic Computing 8, no. 4 (October 2017): 27–40. http://dx.doi.org/10.4018/ijamc.2017100102.

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The software is growing in size and complexity every day due to which strong need is felt by the research community to search for the techniques which can optimize test cases effectively. The current study is inspired by the collective behavior of finding paths from the colony of food and uses different versions of Hill Climbing Algorithm (HCA) such as Stochastic, and Steepest Ascent HCA for the purpose of finding a good optimal solution. The performance of the proposed algorithm is verified on the basis of three parameters comprising of optimized test cases, time is taken during the optimization process, and the percentage of optimization achieved. The results suggest that proposed Stochastic HCA is significantly average percentage better than Steepest Ascent HCA in reducing the number of test cases in order to accomplish the optimization target.

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Al-Betar, Mohammed Azmi, Ibrahim Aljarah, Mohammed A. Awadallah, Hossam Faris, and Seyedali Mirjalili. "Adaptive $$\beta -$$ β - hill climbing for optimization." Soft Computing 23, no. 24 (March 9, 2019): 13489–512. http://dx.doi.org/10.1007/s00500-019-03887-7.

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Vaughan, Diane E., Sheldon H. Jacobson, and Derek E. Armstrong. "A New Neighborhood Function for Discrete Manufacturing Process Design Optimization Using Generalized Hill Climbing Algorithms." Journal of Mechanical Design 122, no. 2 (March 1, 2000): 164–71. http://dx.doi.org/10.1115/1.533566.

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Discrete manufacturing process design optimization can be difficult, due to the large number of manufacturing process design sequences and associated input parameter setting combinations that exist. Generalized hill climbing algorithms have been introduced to address such manufacturing design problems. Initial results with generalized hill climbing algorithms required the manufacturing process design sequence to be fixed, with the generalized hill climbing algorithm used to identify optimal input parameter settings. This paper introduces a new neighborhood function that allows generalized hill climbing algorithms to be used to also identify the optimal discrete manufacturing process design sequence among a set of valid design sequences. The neighborhood function uses a switch function for all the input parameters, hence allows the generalized hill climbing algorithm to simultaneously optimize over both the design sequences and the inputs parameters. Computational results are reported with an integrated blade rotor discrete manufacturing process design problem under study at the Materials Process Design Branch of the Air Force Research Laboratory, Wright Patterson Air Force Base (Dayton, Ohio, USA). [S1050-0472(00)01002-3]

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John, Collether. "High Speed Hill Climbing Algorithm for Portfolio Optimization." Tanzania Journal of Science 47, no. 3 (August 15, 2021): 1236–42. http://dx.doi.org/10.4314/tjs.v47i3.31.

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Portfolio can be defined as a collection of investments. Portfolio optimization usually is about maximizing expected return and/or minimising risk of a portfolio. The mean-variance model makes simplifying assumptions to solve portfolio optimization problem. Presence of realistic constraints leads to a significant different and complex problem. Also, the optimal solution under realistic constraints cannot always be derived from the solution for the frictionless market. The heuristic algorithms are alternative approaches to solve the extended problem. In this research, a heuristic algorithm is presented and improved for higher efficiency and speed. It is a hill climbing algorithm to tackle the extended portfolio optimization problem. The improved algorithm is Hill Climbing Simple–with Reducing Thresh-hold Percentage, named HC-S-R. It is applied in standard portfolio optimization problem and benchmarked with the quadratic programing method and the Threshold Accepting algorithm, a well-known heuristic algorithm for portfolio optimization problem. The results are also compared with its original algorithm HC-S. HC-S-R proves to be a lot faster than HC-S and TA and more effective and efficient than TA. Keywords: Portfolio optimization; Hill climbing algorithm; Threshold percentage; Reducing sequence; Threshold Acceptance algorithm

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Anam, Hairul, Feby Sabilhul Hanafi, Ahmad Fauzal Adifia, Ahmad Firdaus Ababil, and Saiful Bukhori. "Penerapan Metode Steepest Ascent Hill Climb pada Permainan Puzzle." INFORMAL: Informatics Journal 3, no. 2 (August 31, 2018): 36. http://dx.doi.org/10.19184/isj.v3i2.9987.

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Puzzle is one example of the application of artificial intelligence, in the process of completion there are many search algorithms that can be applied. The 8 puzzle solution will be faster obtained if the array principle is used with a variation of the Steepest-Ascent Hill Climbing (Hill Climbing algorithm by choosing the sharpest / steepest slope) with the correct heuristic parameters and distance heuristics and combined with LogList as the storage state ever passed to overcome the problems in the hill climbing algorithm itself and avoid the looping state that has been passed. Steepest Ascent Hill Climbing is an algorithm method that is widely used for optimization problems. The application of the SAHC (Steepest Ascent Hill Climbing) Algorithm to the puzzle is needed so that the game is completed with optimal time.

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Fronita, Mona, Rahmat Gernowo, and Vincencius Gunawan. "Comparison of Genetic Algorithm and Hill Climbing for Shortest Path Optimization Mapping." E3S Web of Conferences 31 (2018): 11017. http://dx.doi.org/10.1051/e3sconf/20183111017.

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Traveling Salesman Problem (TSP) is an optimization to find the shortest path to reach several destinations in one trip without passing through the same city and back again to the early departure city, the process is applied to the delivery systems. This comparison is done using two methods, namely optimization genetic algorithm and hill climbing. Hill Climbing works by directly selecting a new path that is exchanged with the neighbour’s to get the track distance smaller than the previous track, without testing. Genetic algorithms depend on the input parameters, they are the number of population, the probability of crossover, mutation probability and the number of generations. To simplify the process of determining the shortest path supported by the development of software that uses the google map API. Tests carried out as much as 20 times with the number of city 8, 16, 24 and 32 to see which method is optimal in terms of distance and time computation. Based on experiments conducted with a number of cities 3, 4, 5 and 6 producing the same value and optimal distance for the genetic algorithm and hill climbing, the value of this distance begins to differ with the number of city 7. The overall results shows that these tests, hill climbing are more optimal to number of small cities and the number of cities over 30 optimized using genetic algorithms.

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Dunn, N. A., J. S. Conery, and S. R. Lockery. "Circuit Motifs for Spatial Orientation Behaviors Identified by Neural Network Optimization." Journal of Neurophysiology 98, no. 2 (August 2007): 888–97. http://dx.doi.org/10.1152/jn.00074.2007.

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Spatial orientation behavior is universal among animals, but its neuronal basis is poorly understood. The main objective of the present study was to identify candidate patterns of neuronal connectivity (motifs) for two widely recognized classes of spatial orientation behaviors: hill climbing, in which the organism seeks the highest point in a spatial gradient, and goal seeking, in which the organism seeks an intermediate point in the gradient. Focusing on simple networks of graded processing neurons characteristic of Caenorhabditis elegans and other nematodes, we used an unbiased optimization algorithm to seek values of neuronal time constants, resting potentials, and synaptic strengths sufficient for each type of behavior. We found many different hill-climbing and goal-seeking networks that performed equally well in the two tasks. Surprisingly, however, each hill-climbing network represented one of just three fundamental circuit motifs, and each goal-seeking network comprised two of these motifs acting in concert. These motifs are likely to inform the search for the real circuits that underlie these behaviors in nematodes and other organisms.

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Zhang, Xing Wen. "Does Complex Metaheuristic Out-Perform Simple Hill-Climbing for Optimization Problems? A Simulation Evaluation." Advanced Materials Research 748 (August 2013): 666–69. http://dx.doi.org/10.4028/www.scientific.net/amr.748.666.

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In this paper we compare the performance of metaheuristic methods, namely simulated annealing and Tabu Search, against simple hill climbing heuristic on a supply chain optimization problem. The benchmark problem we consider is the retailer replenishment optimization problem for a retailer selling multiple products. Computation and simulation results demonstrate that simulated annealing and Tabu search improve solution quality. However, the performance improvement is less in simulations with random noise. Lastly, simulated annealing appears to be more robust than Tabu search, and the results justify its extra implementation effort and computation time when compared against hill climbing.

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Jacobson, Sheldon H., and Enver Y¨cesan. "Global Optimization Performance Measures for Generalized Hill Climbing Algorithms." Journal of Global Optimization 29, no. 2 (June 2004): 173–90. http://dx.doi.org/10.1023/b:jogo.0000042111.72036.11.

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Dissertations / Theses on the topic "Hill-climbing optimization"

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Johnson, Alan W. "Generalized hill climbing algorithms for discrete optimization problems." Diss., This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-06062008-152638/.

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Sullivan, Kelly Ann. "A Convergence Analysis of Generalized Hill Climbing Algorithms." Diss., Virginia Tech, 1999. http://hdl.handle.net/10919/27027.

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Generalized hill climbing (GHC) algorithms provide a unifying framework for describing several discrete optimization problem local search heuristics, including simulated annealing and tabu search. A necessary and a sufficient convergence condition for GHC algorithms are presented. The convergence conditions presented in this dissertation are based upon a new iteration classification scheme for GHC algorithms. The convergence theory for particular formulations of GHC algorithms is presented and the implications discussed. Examples are provided to illustrate the relationship between the new convergence conditions and previously existing convergence conditions in the literature. The contributions of the necessary and the sufficient convergence conditions for GHC algorithms are discussed and future research endeavors are suggested.
Ph. D.

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Vaughan, Diane Elizabeth. "Simultaneous Generalized Hill Climbing Algorithms for Addressing Sets of Discrete Optimization Problems." Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/28514.

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Generalized hill climbing (GHC) algorithms provide a framework for using local search algorithms to address intractable discrete optimization problems. Many well-known local search algorithms can be formulated as GHC algorithms, including simulated annealing, threshold accepting, Monte Carlo search, and pure local search (among others). This dissertation develops a mathematical framework for simultaneously addressing a set of related discrete optimization problems using GHC algorithms. The resulting algorithms, termed simultaneous generalized hill climbing (SGHC) algorithms, can be applied to a wide variety of sets of related discrete optimization problems. The SGHC algorithm probabilistically moves between these discrete optimization problems according to a problem generation probability function. This dissertation establishes that the problem generation probability function is a stochastic process that satisfies the Markov property. Therefore, given a SGHC algorithm, movement between these discrete optimization problems can be modeled as a Markov chain. Sufficient conditions that guarantee that this Markov chain has a uniform stationary probability distribution are presented. Moreover, sufficient conditions are obtained that guarantee that a SGHC algorithm will visit the globally optimal solution over all the problems in a set of related discrete optimization problems. Computational results are presented with SGHC algorithms for a set of traveling salesman problems. For comparison purposes, GHC algorithms are also applied individually to each traveling salesman problem. These computational results suggest that optimal/near optimal solutions can often be reached more quickly using a SGHC algorithm.
Ph. D.

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Singh, Vinay. "Design and Shape Optimization of Unmanned, Semi-Rigid Airship for Rapid Descent Using Hybrid Genetic Algorithm." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/38673.

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Airships provide an eco-friendly and cost-effective means to suit sustained airborne operations. Smaller autonomous airships are highly susceptible to adverse atmospheric conditions owing to their under-actuated, underpowered and bulky size relative to other types of unmanned aerial vehicles (UAVs). To mitigate these limitations, careful considerations of the size and shape must be made at the design stage. This research presents a methodology for obtaining an optimized shape of a semi-rigid airship. Rapid descent of the LTA ship is achieved by means of a moving gondola attached to a rigid keel mounted under the helium envelope from the bow to the mid-section of the hull. The study entails the application of a robust hybrid genetic algorithm (HGA) for the multi-disciplinary design and optimization of an airship capable of rapid descent, with lower drag and optimum surface area. A comprehensive sensitivity analysis was also performed on the basis of algorithmic parameters and atmospheric conditions. With the help of HGA, a semi-rigid airship capable of carrying a payload of 0.25 kg to 1.0 kg and capable of pitching at right angles is conceptually designed. The algorithm is also tested on commercially available vehicles to validate the results. In multi-objective optimization problems (MOOPs), the significance of different objectives is dependent on the user.

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Malleypally, Vinaya. "Parallelizing Tabu Search Based Optimization Algorithm on GPUs." Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7638.

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There are many combinatorial optimization problems such as traveling salesman problem, quadratic-assignment problem, flow shop scheduling, that are computationally intractable. Tabu search based simulated annealing is a stochastic search algorithm that is widely used to solve combinatorial optimization problems. Due to excessive run time, there is a strong demand for a parallel version that can be applied to any problem with minimal modifications. Existing advanced and/or parallel versions of tabu search algorithms are specific to the problem at hand. This leads to a drawback of optimization only for that particular problem. In this work, we propose a parallel version of tabu search based SA on the Graphics Processing Unit (GPU) platform. We propose two variants of the algorithm based on where the tabu list is stored (global vs. local). In the first version, the list is stored in the global shared memory such that all threads can access this list. Multiple random walks in solution space are carried out. Each walk avoids the moves made in rest of the walks due to their access to global tabu list at the expense of more time. In the second version, the list is stored at the block level and is shared by only the block threads. Groups of random walks are performed in parallel and a walk in a group avoids the moves made by the rest of the walks within that group due to their access to shared local tabu list. This version is better than the first version in terms of execution time. On the other hand, the first version finds the global optima more often. We present experimental results for six difficult optimization functions with known global optima. Compared to the CPU implementation with similar workload, the proposed GPU versions are faster by approximately three orders of magnitude and often find the global optima.

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Henderson, Darrall. "Assessing the Finite-Time Performance of Local Search Algorithms." Diss., Virginia Tech, 2001. http://hdl.handle.net/10919/26926.

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Identifying a globally optimal solution for an intractable discrete optimization problem is often cost prohibitive. Therefore, solutions that are within a predetermined threshold are often acceptable in practice. This dissertation introduces the concept of B-acceptable solutions where B is a predetermined threshold for the objective function value. It is difficult to assess a priori the effectiveness of local search algorithms, which makes the process of choosing parameters to improve their performance difficult. This dissertation introduces the B-acceptable solution probability in terms of B-acceptable solutions as a finite-time performance measure for local search algorithms. The B-acceptable solution probability reflects how effectively an algorithm has performed to date and how effectively an algorithm can be expected to perform in the future. The B-acceptable solution probability is also used to obtain necessary asymptotic convergence (with probability one) conditions. Upper and lower bounds for the B-acceptable solution probability are presented. These expressions assume particularly simple forms when applied to specific local search strategies such as Monte Carlo search and threshold accepting. Moreover, these expressions provide guidelines on how to manage the execution of local search algorithm runs. Computational experiments are reported to estimate the probability of reaching a B-acceptable solution for a fixed number of iterations. Logistic regression is applied as a tool to estimate the probability of reaching a B-acceptable solution for values of B close to the objective function value of a globally optimal solution as well as to estimate this objective function value. Computational experiments are reported with logistic regression for pure local search, simulated annealing and threshold accepting applied to instances of the TSP with known optimal solutions.
Ph. D.

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Burnett, Linda Dee. "Heuristic Optimization of Boolean Functions and Substitution Boxes for Cryptography." Queensland University of Technology, 2005. http://eprints.qut.edu.au/16023/.

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Fundamental to the electronic security of information and communication systems, is the correct use and application of appropriate ciphers. The strength of these ciphers, particularly in their ability to resist cryptanalytic attacks, directly in uences the overall strength of the entire system. The strength of the underlying cipher is reliant upon a robust structure and the carefully designed interaction between components in its architecture. Most importantly, however, cipher strength is critically dependent on the strength of the individual components of which it is comprised. Boolean functions and substitution boxes (s-boxes) are among the most common and essential components of ciphers. This is because they are able to provide a cipher with strengthening properties to resist known and potential cryptanalytic attacks. Thus, it is not surprising that significant research effort has been made in trying to develop ways of obtaining boolean functions and substitution boxes with optimal achievable measures of desirable cryptographic properties. Three of the main cryptographic properties required by strong boolean functions and s-boxes are nonlinearity, correlation immunity and propagation criteria, with different cryptographic applications requiring different acceptable measures of these and other properties. As combinations of cryptographic properties exhibited by functions can be conicting, finding cryptographically strong functions often means that a trade-off needs to be made when optimizing property values. Throughout this thesis, the term "optimization" specifically refers to seeking to obtain the best achievable combination of target property values which may be exhibited by boolean functions and s-boxes, regardless of whether the relevant properties are conflicting or complementary. This thesis focusses on a particular class of techniques for obtaining strong functions for cryptographic applications, referred to as heuristic methods or, simply, heuristics. Three new heuristic methods, each aimed at generating boolean functions optimizing one or more of the main cryptographic properties mentioned above, in addition to other desirable properties, are presented. The first of the new heuristic methods developed for this thesis focusses on generating boolean functions which are balanced and exhibit very high nonlinearities. Highly nonlinear balanced functions are critical to many cryptographic applications, as they provide good resistance to linear cryptanalytic attacks. This first method is based on the recursive modification of a starting bent function and is shown to be highly successful and efficient at generating numerous such functions, which also exhibit low autocorrelation values, in a very short computational time. The generation of balanced, correlation immune boolean functions that also exhibit the confl icting property of high nonlinearity is the focus of the second new heuristic method developed for this thesis. By concatenating selected pairs of lower-dimensional boolean functions together in the Walsh Hadamard transform domain, direct optimization for both resilience and nonlinearity was able to take place at each level towards and for the final function. This second method was able to generate examples of boolean functions with almost all of the best known optimal combinations of target property values. Experiments have shown the success of this method in consistently generating highly nonlinear resilient boolean functions, for a range of orders of resilience, with such functions possessing optimal algebraic degree. A third new heuristic method, which searches for balanced boolean functions which satisfy a non-zero degree of propagation criteria and exhibit high nonlinearity, is presented. Intelligent bit manipulations in the truth table of starting functions, based on fundamental relationships between boolean function transforms and measures, provide the design rationale for this method. Two new function generation schemes have been proposed for this method, to efficiently satisfy the requirements placed on the starting functions utilized in the computational process. An optional process attempts to increase the algebraic degree of the resulting functions, without sacrificing the optimalities that are achievable. The validity of this method is demonstrated through the success of various experimental trials. Switching the focus from single output boolean functions to multiple output boolean functions (s-boxes), the effectiveness of existing heuristic techniques (namely Genetic Algorithm, Hill Climbing Method and combined Genetic Algorithm/Hill Climbing) in primarily being applied to improve the nonlinearity of s-boxes of various dimensions, is investigated. The prior success of these heuristic techniques for improving the nonlinearity of boolean functions has been previously demonstrated, as has the success of hill climbing in isolation when applied to bijective s-boxes. An extension to the bijective s-box optimization work is presented in this thesis. In this new research, a Genetic Algorithm, Hill Climbing Method and the two in combination are applied to the nonlinearity and autocorrelation optimization of regular NxM s-boxes (N > M) to investigate the effectiveness and efficiency of each of these heuristics. A new breeding scheme, utilized in the Genetic Algorithm and combined Genetic Algorithm/Hill Climbing trials, is also presented. The success of experimental results compared to random regular s-box generation is demonstrated. New research in applying the Hill Climbing Method to construct NxM sboxes (N > M) required to meet specific property criteria is presented. The consideration of the characteristics desired by the constructed s-boxes largely dictated the generation process. A discussion on the generation process of the component functions is included. Part of the results produced by experimental trials were incorporated into a commonly used family of stream ciphers, thus further supporting the use of heuristic techniques as a useful means of obtaining strong functions suitable for incorporation into practical ciphers. An analysis of the cryptographic properties of the s-box used in the MARS block cipher, the method of generation and the computational time taken to obtain this s-box, led to the new research reported in this thesis on the generation of MARS-like s-boxes. It is shown that the application of the Hill Climbing Method, with suitable requirements placed on the component boolean functions, was able to generate multiple MARS-like s-boxes which satisfied the MARS sbox requirements and provided additional properties. This new work represented an alternative approach to the generation of s-boxes satisfying the MARS sbox property requirements but which are cryptographically superior and can be obtained in a fraction of the time than that which was taken to produce the MARS s-box. An example MARS-like s-box is presented in this thesis. The overall value of heuristic methods in generating strong boolean functions and substitution boxes is clearly demonstrated in this thesis. This thesis has made several significant contributions to the field, both in the development of new, specialized heuristic methods capable of generating strong boolean functions, and in the analysis and optimization of substitution boxes, the latter achieved through applying existing heuristic techniques.

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Olivieri, Julia. "Drawing DNA Sequence Networks." Oberlin College Honors Theses / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=oberlin1466511242.

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Ferreira, Alexandre Beletti [UNESP]. "Avaliação de operadores de algoritmos genéticos em otimização multidimensional." Universidade Estadual Paulista (UNESP), 2007. http://hdl.handle.net/11449/88880.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Desenvolveu-se neste trabalho a implementação computacional de um algoritmo genético. Este se constituiu de uma população inicial sobre a qual agem quatro operadores fundamentais: seleção, “crossover”, substituição e mutação, e produz uma nova população. Sobre a qual agem novamente os operadores genéticos, e assim sucessivamente produzindo uma seqüência de populações. O operador seleção foi implementado em três algoritmos básicos: roda da roleta, amostragem estatística universal e torneio. O “crossover” também foi desenvolvido em algumas opções: um ponto, dois pontos, múltiplos pontos, e uniforme. A substituição de indivíduos da população pelos filhos ocorre de três maneiras básicas: dos pais, dos menos aptos, e dos indivíduos sorteados aleatoriamente. A mutação ocorre de apenas uma maneira. Inicialmente, o algoritmo genético foi executado em computador de maneira seqüencial. Resolveu-se um conjunto de problemas de otimização multidimensional e também o Problema do Caixeiro Viajante (TSP – Traveler Salesman Problem). Fez-se um estudo paramétrico dos vários parâmetros que aparecem no algoritmo genético, tais como: tamanho da população, número de gerações, taxa de seleção, probabilidade de mutação, e taxa de elitismo. No caso de problemas de otimização multidimensional a representação do cromossomo de cada indivíduo é binária, já no caso do TSP a representação é inteira decimal. Em ambos os casos da otimização multidimensional e do TSP também foi utilizada a técnica de hill-climbing visando aumentar a taxa de convergência da solução. A técnica de janelamento foi utilizada somente no caso de otimização multidimensional, também visando aumentar a taxa de convergência. Posteriormente, o algoritmo genético foi executado também em processamento computacional paralelo,...
It was developed in this work the computational implementation of a genetic algorithm. That is constituted of an initial population upon which act four basic operators: selection, crossover, substitution and mutation, producing a new population. Upon which act again the genetic operators, and thus, successively, producing a sequence of populations. The operator selection was implemented in three basic algorithms: roulette wheel, stochastic universal sampling, and tournament. The crossover also was developed in some options: one point, two points, several points, and uniform. Substitution of individuals from the population by the newborns happens in three basic ways: the fathers, the less apt, and the individuals sorted randomly. Mutation happens in only one manner. Initially, the genetic algorithm was processed sequentially in the computer. It was solved a set of multidimensional optimization problems and also the Traveler Salesman Problem - TSP. It was done a parametric study of the several parameters that appear in the genetic algorithm, such as: population size, number of generations, selection rate, mutation probability, and elitism rate. In the case of multidimensional optimization problems the chromosome representation of each individual is binary, but in the case of TSP the representation is integer decimal. In both cases of multidimensional optimization and TSP also it were used the hill-climbing technique aiming to increase the solution convergence rate. The windowing technique was used just for the multidimensional optimization case, also aiming to increase the convergence rate. Lately, the genetic algorithm was also performed in a computational parallel processing mode, using several computers linked by a net. In each computer it was executed one genetic algorithm upon a local population. The interaction among several populations was done through the migration ...(Complete abstract, click electronic access below)

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Vaneman, Warren Kenneth. "Evaluating System Performance in a Complex and Dynamic Environment." Diss., Virginia Tech, 2002. http://hdl.handle.net/10919/30043.

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Realistically, organizational and/or system efficiency performance is dynamic, non-linear, and a function of multiple interactions in production. However, in the efficiency literature, system performance is frequently evaluated considering linear combinations of the input/output variables, without explicitly taking into account the interactions and feedback mechanisms that explain the causes of efficiency behavior, the dynamic nature of production, and non-linear combinations of the input/output variables. Consequently, policy decisions based on these results may be sub-optimized because the non-linear relationships among variables, causal relationships, and feedback mechanisms are ignored. This research takes the initial steps of evaluating system efficiency performance in a dynamic environment, by relating the factors that effect system efficiency performance to the policies that govern it. First, this research extends the concepts of the static production axioms into a dynamic realm, where inputs are not instantaneously converted into outputs. The relationships of these new dynamic production axioms to the basic behaviors associated with system dynamics structures are explored. Second, this research introduces a methodological approach that combines system dynamics modeling with the measurement of productive efficiency. System dynamics is a modeling paradigm that evaluates system policies by exploring the causal relationships of the important elements within the system. This paradigm is coupled with the fundamental assumptions of production theory in order to evaluate the productive efficiency of a production system operating within a dynamic and non-linear environment. As a result, a subsystem within the system dynamics model is introduced that computes efficiency scores based on the fundamental notions of productive efficiency. The framework's ability to combine prescriptive and descriptive modeling characteristics, as well as dynamic and combinatorial complexity, can potentially have a greater impact on policy decisions and how they affect system efficiency performance. Finally, the utility of these concepts is demonstrated in an implementation case study. This methodology generates a prescriptive dynamical production frontier which defines the optimal production resources required to satisfy system requirements. Additionally, the dynamical production frontier allows for analysis for comparisons between options during a transient period, insight into possible unintended consequences, and the ability to forecast optimal times for introducing system or process improvements.
Ph. D.

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Books on the topic "Hill-climbing optimization"

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Generalized Hill Climbing Algorithms For Discrete Optimization Problems. Storming Media, 1997.

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Okasha, Samir. Wright’s Adaptive Landscape, Fisher’s Fundamental Theorem. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198815082.003.0004.

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Fitness maximization, or optimization, is a controversial idea in evolutionary biology. One classical formulation of this idea is that natural selection will tend to push a population up a peak in an adaptive landscape, as Sewall Wright first proposed. However, the hill-climbing property only obtains under particular conditions, and even then the ascent is not usually by the steepest route; this shows why it is misleading to assimilate the process of natural selection to a process of goal-directed choice. A different formulation of the idea of fitness-maximization is R. A. Fisher’s ‘fundamental theorem of natural selection’. However, the theorem points only to a weak sense in which selection is an optimizing process, for it requires that ‘environmental constancy’ be understood in a highly specific way. It does not vindicate the claim that natural selection has an intrinsic tendency to produce adaptation.

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Book chapters on the topic "Hill-climbing optimization"

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Impagliazzo, Russell. "Hill-Climbing vs. Simulated Annealing for Planted Bisection Problems." In Approximation, Randomization, and Combinatorial Optimization: Algorithms and Techniques, 2–5. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44666-4_2.

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Hifi, Mhand, and Labib Yousef. "Handling Lower Bound and Hill-Climbing Strategies for Sphere Packing Problems." In Recent Advances in Computational Optimization, 145–64. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21133-6_9.

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Hirata, Aoto, Tetsuya Oda, Nobuki Saito, Masaharu Hirota, and Kengo Katayama. "A Coverage Construction Method Based Hill Climbing Approach for Mesh Router Placement Optimization." In Lecture Notes in Networks and Systems, 355–64. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61108-8_35.

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Marwala, Tshilidzi, and Monica Lagazio. "Particle Swarm Optimization and Hill-Climbing Optimized Rough Sets for Modeling Interstate Conflict." In Advanced Information and Knowledge Processing, 147–64. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-790-7_8.

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Cohen, Aviad, Alexander Nadel, and Vadim Ryvchin. "Local Search with a SAT Oracle for Combinatorial Optimization." In Tools and Algorithms for the Construction and Analysis of Systems, 87–104. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72013-1_5.

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AbstractNP-hard combinatorial optimization problems are pivotal in science and business. There exists a variety of approaches for solving such problems, but for problems with complex constraints and objective functions, local search algorithms scale the best. Such algorithms usually assume that finding a non-optimal solution with no other requirements is easy. However, what if it is NP-hard? In such case, a SAT solver can be used for finding the initial solution, but how can one continue solving the optimization problem? We offer a generic methodology, called Local Search with SAT Oracle (), to solve such problems. facilitates implementation of advanced local search methods, such as variable neighbourhood search, hill climbing and iterated local search, while using a SAT solver as an oracle. We have successfully applied our approach to solve a critical industrial problem of cell placement and productized our solution at Intel.

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Calderín, Jenny Fajardo, Antonio D. Masegosa, Alejandro Rosete Suárez, and David A. Pelta. "Adaptation Schemes and Dynamic Optimization Problems: A Basic Study on the Adaptive Hill Climbing Memetic Algorithm." In Nature Inspired Cooperative Strategies for Optimization (NICSO 2013), 85–97. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01692-4_7.

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Zhang, Zhenya, Deyun Lyu, Paolo Arcaini, Lei Ma, Ichiro Hasuo, and Jianjun Zhao. "Effective Hybrid System Falsification Using Monte Carlo Tree Search Guided by QB-Robustness." In Computer Aided Verification, 595–618. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_29.

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AbstractHybrid system falsification is an important quality assurance method for cyber-physical systems with the advantage of scalability and feasibility in practice than exhaustive verification. Falsification, given a desired temporal specification, tries to find an input of violation instead of a proof guarantee. The state-of-the-art falsification approaches often employ stochastic hill-climbing optimization that minimizes the degree of satisfaction of the temporal specification, given by its quantitative robust semantics. However, it has been shown that the performance of falsification could be severely affected by the so-called scale problem, related to the different scales of the signals used in the specification (e.g., rpm and speed): in the robustness computation, the contribution of a signal could be masked by another one. In this paper, we propose a novel approach to tackle this problem. We first introduce a new robustness definition, called QB-Robustness, which combines classical Boolean satisfaction and quantitative robustness. We prove that QB-Robustness can be used to judge the satisfaction of the specification and avoid the scale problem in its computation. QB-Robustness is exploited by a falsification approach based on Monte Carlo Tree Search over the structure of the formal specification. First, tree traversal identifies the sub-formulas for which it is needed to compute the quantitative robustness. Then, on the leaves, numerical hill-climbing optimization is performed, aiming to falsify such sub-formulas. Our in-depth evaluation on multiple benchmarks demonstrates that our approach achieves better falsification results than the state-of-the-art falsification approaches guided by the classical quantitative robustness, and it is largely not affected by the scale problem.

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López-Pintado, Orlenys, Marlon Dumas, Maksym Yerokhin, and Fabrizio Maria Maggi. "Silhouetting the Cost-Time Front: Multi-objective Resource Optimization in Business Processes." In Lecture Notes in Business Information Processing, 92–108. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85440-9_6.

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AbstractThe allocation of resources in a business process determines the trade-off between cycle time and resource cost. A higher resource utilization leads to lower cost and higher cycle time, while a lower resource utilization leads to higher cost and lower waiting time. In this setting, this paper presents a multi-objective optimization approach to compute a set of Pareto-optimal resource allocations for a given process concerning cost and cycle time. The approach heuristically searches through the space of possible resource allocations using a simulation model to evaluate each allocation. Given the high number of possible allocations, it is imperative to prune the search space. Accordingly, the approach incorporates a method that selectively perturbs a resource utilization to derive new candidates that are likely to Pareto-dominate the already explored ones. The perturbation method relies on two indicators: resource utilization and resource impact, the latter being the contribution of a resource to the cost or cycle time of the process. Additionally, the approach incorporates a ranking method to accelerate convergence by guiding the search towards the resource allocations closer to the current Pareto front. The perturbation and ranking methods are embedded into two search meta-heuristics, namely hill-climbing and tabu-search. Experiments show that the proposed approach explores fewer resource allocations to compute Pareto fronts comparable to those produced by a well-known genetic algorithm for multi-objective optimization, namely NSGA-II.

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Sakamoto, Shinji, Seiji Ohara, Leonard Barolli, and Shusuke Okamoto. "Performance Evaluation of WMNs Using an Hybrid Intelligent System Based on Particle Swarm Optimization and Hill Climbing Considering Different Number of Iterations." In Advances in Internet, Data and Web Technologies, 138–49. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39746-3_15.

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Hirata, Aoto, Tetsuya Oda, Nobuki Saito, Yuki Nagai, Masaharu Hirota, Kengo Katayama, and Leonard Barolli. "A Coverage Construction and Hill Climbing Approach for Mesh Router Placement Optimization: Simulation Results for Different Number of Mesh Routers and Instances Considering Normal Distribution of Mesh Clients." In Complex, Intelligent and Software Intensive Systems, 161–71. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79725-6_16.

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Conference papers on the topic "Hill-climbing optimization"

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Xu, Dongxin, Hsiao-Chun Wu, and Chong-Yung Chi. "Blind Separation and Equalization Using Novel Hill-Climbing Optimization." In 2007 41st Asilomar conference on Signals, Systems and Computers (ACSSC). IEEE, 2007. http://dx.doi.org/10.1109/acssc.2007.4487154.

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Huiying Xu and Zheng Zhou. "Hill-climbing genetic algorithm optimization in cognitive radio decision engine." In 2013 15th IEEE International Conference on Communication Technology (ICCT). IEEE, 2013. http://dx.doi.org/10.1109/icct.2013.6820357.

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Shehab, Mohammad, Ahamad Tajudin Khader, Mohammed Azmi Al-Betar, and Laith Mohammad Abualigah. "Hybridizing cuckoo search algorithm with hill climbing for numerical optimization problems." In 2017 8th International Conference on Information Technology (ICIT). IEEE, 2017. http://dx.doi.org/10.1109/icitech.2017.8079912.

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Bharti and Dheeraj Kumar Sharma. "Searching boolean function using simulated annealing and hill climbing optimization techniques." In 2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT). IEEE, 2016. http://dx.doi.org/10.1109/icaccct.2016.7831601.

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Rahim, Siti Khatijah Nor Abdul, Andrzej Bargiela, and Rong Qu. "The incorporation of late acceptance hill climbing strategy in the deterministic optimization of examination scheduling framework: A comparison with the traditional hill climbing." In 2014 IEEE Conference on Systems, Process and Control (ICSPC). IEEE, 2014. http://dx.doi.org/10.1109/spc.2014.7086247.

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"Hill Climbing versus Genetic Algorithm Optimization in Solving the Examination Timetabling Problem." In International Conference on Operations Research and Enterprise Systems. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004286600430052.

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Ravindran, Vineetha, and Jil Sutaria. "Implementation in arm microcontroller to maximize the power output of solar panel using Hill Climbing Algorithm." In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). IEEE, 2016. http://dx.doi.org/10.1109/iceeot.2016.7754880.

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Hayakawa, Daiki, Kazunori Mizuno, Hitoshi Sasaki, and Seiichi Nishihara. "Improving Search Efficiency Adopting Hill-Climbing to Ant Colony Optimization for Constraint Satisfaction Problems." In 2011 Third International Conference on Knowledge and Systems Engineering (KSE). IEEE, 2011. http://dx.doi.org/10.1109/kse.2011.39.

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Tuong Quan Vo, Hyoung Seok Kim, and Byung Ryong Lee. "Parameter optimization of fish robot’s smooth gaiting using Hill Climbing - Genetic Algorithm." In 2008 International Conference on Control, Automation and Systems (ICCAS). IEEE, 2008. http://dx.doi.org/10.1109/iccas.2008.4694333.

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Maiorana, Emanuele, Gabriel E. Hine, and Patrizio Campisi. "Hill-climbing attack: Parametric optimization and possible countermeasures. An application to on-line signature recognition." In 2013 International Conference on Biometrics (ICB). IEEE, 2013. http://dx.doi.org/10.1109/icb.2013.6612961.

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Reports on the topic "Hill-climbing optimization"

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Jacobson, Sheldon H. Designing Optimal Generalized Hill Climbing Algorithms with Applications to Discrete Manufacturing Process Design Optimization. Fort Belvoir, VA: Defense Technical Information Center, November 2003. http://dx.doi.org/10.21236/ada419522.

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