Adaptive genetic algorithm using harmony search
07 Jul 2010-pp 819-820
TL;DR: This paper proposes an adaptive parameter controlling approach using harmony search that directs the search from the current state to a desired state by determining suitable parameter values such that the balance between exploration and exploitation is suitable for that state transition.
Abstract: Evolutionary algorithm is one of the major classes of stochastic search methods. This algorithm searches the problem space by exploring and exploiting the search space. The balance between exploration and exploitation will change throughout the search process. Maintaining the right balance between the exploration and exploitation in the search process is crucial for the success of the search process. The parameter values of the algorithm play a crucial role in determining the nature of the search, whether explorative or exploitative. In this paper, we propose an adaptive parameter controlling approach using harmony search. During the search process, harmony search directs the search from the current state to a desired state by determining suitable parameter values such that the balance between exploration and exploitation is suitable for that state transition. The preliminary results of the proposed method is comparable with those from the literature.
Citations
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TL;DR: An overview of improvements in terms of parameters setting and hybridizing HS components with other metaheuristic algorithms is presented, with a goal of providing useful references to fundamental concepts accessible to the broad community of optimization practitioners.
Abstract: The harmony search (HS) algorithm is a relatively new population-based metaheuristic optimization algorithm. It imitates the music improvisation process where musicians improvise their instruments' pitch by searching for a perfect state of harmony. Since the emergence of this algorithm in 2001, it attracted many researchers from various fields especially those working on solving optimization problems. Consequently, this algorithm guided researchers to improve on its performance to be in line with the requirements of the applications being developed. These improvements primarily cover two aspects: (1) improvements in terms of parameters setting, and (2) improvements in terms of hybridizing HS components with other metaheuristic algorithms. This paper presents an overview of these aspects, with a goal of providing useful references to fundamental concepts accessible to the broad community of optimization practitioners.
206 citations
Cites methods from "Adaptive genetic algorithm using ha..."
...…stored in the HM is mimicked to improve the GA selection mechanism Li et al. (2008) GA+HS HS is used to maintain a balance between the exploration and exploitation concepts in GA Nadi et al. (2010) LDA+HS HS is used as a preprocessing technique to overcome the LDA’s problem Moeinzadeh et al. (2009)...
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...In order to improve the performance of the evolutionary algorithm, Nadi et al. (2010) proposed a new technique that maintains the right balance between the exploration and exploitation of the evolutionary algorithm in the search process....
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TL;DR: This paper presents a comprehensive overview of the development of the harmony search (HS) algorithm and its applications, and describes the HS algorithm and present how its parameters affect algorithm performance.
Abstract: This paper presents a comprehensive overview of the development of the harmony search (HS) algorithm and its applications. HS is a well-known human-based meta-heuristic algorithm that mimics the process of creating a new harmony in music. This algorithm can be applied to different fields of research, owing to its ability to balance between exploitation (i.e., searching around the known best) and exploration (i.e., roaming the entire search space). Thus, numerous studies have been conducted to utilize HS in real-world optimization problems, and many variants and hybrid algorithms of HS have been developed to cope with different problems. In this paper, HS and its variants are reviewed from various aspects. First, we describe the HS algorithm and present how its parameters affect algorithm performance. Second, we describe HS classifications based on the well-known HS variants and hybrid algorithms, along with their applications. Finally, a discussion conducted on the strengths and weaknesses of the HS algorithm and the possibilities for its improvement. Focusing on related work from diverse fields (such as optimization, engineering, computer science, biology, and medicine), this paper can foster interests on the application of HS for multidisciplinary audiences.
59 citations
TL;DR: This study introduces a hybrid harmony search algorithm (HHSA) as a means to solve ab initio protein tertiary structure prediction problem and shows that the algorithm can find more precise solutions than other previous studies.
Abstract: Predicting the tertiary structure of proteins from their linear sequence is a big challenge in biology. The existing computational methods are not powerful enough to search for the precise structure in a huge conformational space. This inadequate capability of the computational methods, however, is a major obstacle when trying to tackle this problem. The observations of some previous studies have revealed much interest in hybridizing a local search-based metahuristic algorithm within the population-based metahuristic algorithm. This study introduces a hybrid harmony search algorithm (HHSA) as a means to solve ab initio protein tertiary structure prediction problem. In HHSA, the iterated local search (ILS) is incorporated with the harmony search algorithm (HSA) to empower it so as to find the local optimal solution within the search space of the new harmony. Furthermore, the global-best concept of particle swarm optimization (PSO) is incorporated in memory consideration as a selection scheme to accelerate the convergence speed. The HHSA predicts the tertiary structure of a protein giving its sequence alone (i.e., from scratch). Our algorithm converges faster than the classical harmony search algorithm. We evaluate our algorithm using two protein sequences. The results show that our algorithm can find more precise solutions than other previous studies.
24 citations
Cites methods from "Adaptive genetic algorithm using ha..."
...It is hybridized with other successful optimization techniques such as GA (Nadi et al. 2010), particle swarm optimization (PSO) (Omran and Mahdavi, 2008) and Hill climbing (Al-Betar et al....
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...It is hybridized with other successful optimization techniques such as GA (Nadi et al. 2010), particle swarm optimization (PSO) (Omran and Mahdavi, 2008) and Hill climbing (Al-Betar et al. 2012b)....
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TL;DR: This paper solves the well-known conditional and unconditional p -center problem using a modified harmony search algorithm and presents some results for other meta-heuristic algorithms including the variable neighborhood search, the Tabu search, and the scatter search.
23 citations
TL;DR: This article presents a review of hybrid harmony search algorithms, a music-inspired population-based meta-heuristic search and optimization algorithm that combines HSA with other algorithms to improve exploration or global search ability and increase convergence speed.
Abstract: Harmony search algorithm (HSA) is a music-inspired population-based meta-heuristic search and optimization algorithm. In order to improve exploration or global search ability, exploit local search more effectively, increase convergence speed, improve solution quality, and minimize computational cost, researchers have advanced the concept of hybridizing HSA with other algorithms. This article presents a review of hybrid harmony search algorithms.
20 citations
References
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01 Jan 2015
TL;DR: In the second edition, the authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations as discussed by the authors.
Abstract: The overall structure of this new edition is three-tier: Part I presents the basics, Part II is concerned with methodological issues, and Part III discusses advanced topics. In the second edition the authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations. They also added a chapter on problems, reflecting the overall book focus on problem-solvers, a chapter on parameter tuning, which they combined with the parameter control and "how-to" chapters into a methodological part, and finally a chapter on evolutionary robotics with an outlook on possible exciting developments in this field. The book is suitable for undergraduate and graduate courses in artificial intelligence and computational intelligence, and for self-study by practitioners and researchers engaged with all aspects of bioinspired design and optimization.
4,461 citations
Book•
01 Jan 2003
TL;DR: The authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations, and added a chapter on evolutionary robotics with an outlook on possible exciting developments in this field.
Abstract: The overall structure of this new edition is three-tier: Part I presents the basics, Part II is concerned with methodological issues, and Part III discusses advanced topics In the second edition the authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations They also added a chapter on problems, reflecting the overall book focus on problem-solvers, a chapter on parameter tuning, which they combined with the parameter control and "how-to" chapters into a methodological part, and finally a chapter on evolutionary robotics with an outlook on possible exciting developments in this field The book is suitable for undergraduate and graduate courses in artificial intelligence and computational intelligence, and for self-study by practitioners and researchers engaged with all aspects of bioinspired design and optimization
3,364 citations
"Adaptive genetic algorithm using ha..." refers methods in this paper
...Parameter control methods mostly have been categorized into three different subcategories namely deterministic, adaptive, and self-adaptive methods[3, 5]....
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18 May 2009
TL;DR: The most important issues related to tuning EA parameters are discussed, a number of existing tuning methods are described, and a modest experimental comparison among them are presented, hopefully inspiring fellow researchers for further work.
Abstract: Tuning the parameters of an evolutionary algorithm (EA) to a given problem at hand is essential for good algorithm performance. Optimizing parameter values is, however, a non-trivial problem, beyond the limits of human problem solving.In this light it is odd that no parameter tuning algorithms are used widely in evolutionary computing. This paper is meant to be stepping stone towards a better practice by discussing the most important issues related to tuning EA parameters, describing a number of existing tuning methods, and presenting a modest experimental comparison among them. The paper is concluded by suggestions for future research - hopefully inspiring fellow researchers for further work.
268 citations
"Adaptive genetic algorithm using ha..." refers background in this paper
...Feedbacks are mostly based on the quality of the solutions or speed of the algorithm [7]....
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TL;DR: The existing theoretical results are extended in an attempt to provide a broader explanatory and predictive theory of the role of multi-point crossover in genetic algorithms, and the traditional disruption analysis is extended to include two general forms ofMulti- point crossover: n-pointrossover and uniform crossover.
Abstract: On the basis of early theoretical and empirical studies, genetic algorithms have typically used 1 and 2-point crossover operators as the standard mechanisms for implementing recombination. However, there have been a number of recent studies, primarily empirical in nature, which have shown the benefits of crossover operators involving a higher number of crossover points. From a traditional theoretical point of view, the most surprising of these new results relate to uniform crossover, which involves on the averageL/2 crossover points for strings of lengthL. In this paper we extend the existing theoretical results in an attempt to provide a broader explanatory and predictive theory of the role of multi-point crossover in genetic algorithms. In particular, we extend the traditional disruption analysis to include two general forms of multi-point crossover:n-point crossover and uniform crossover. We also analyze two other aspects of multi-point crossover operators, namely, their recombination potential and exploratory power. The results of this analysis provide a much clearer view of the role of multi-point crossover in genetic algorithms. The implications of these results on implementation issues and performance are discussed, and several directions for further research are suggested.
261 citations
"Adaptive genetic algorithm using ha..." refers methods in this paper
...[8] W.M.Spears....
[...]
...[6] K. A. D. Jong and W. M. Spears....
[...]
...For the course of experiments we have used multi-modal problem generator (MPG) of Spears [6, 8]....
[...]
...For the course of experiments we have used multi-modal problem generator (MPG) of Spears [6, 8]....
[...]
Book•
01 Aug 2000
TL;DR: This book provides a characterization of the roles that recombination and mutation play in evolutionary algorithms and introduces new theoretical techniques for studying evolutionary algorithms.
Abstract: Despite decades of work in evolutionary algorithms, there remains an uncertainty as to the relative benefits and detriments of using recombination or mutation. This book provides a characterization of the roles that recombination and mutation play in evolutionary algorithms. It integrates important prior work and introduces new theoretical techniques for studying evolutionary algorithms. Consequences of the theory are explored and a novel method for comparing search and optimization algorithms is introduced. The focus allows the book to bridge multiple communities, including evolutionary biologists and population geneticists.
166 citations
"Adaptive genetic algorithm using ha..." refers methods in this paper
...[8] W.M.Spears....
[...]
...[6] K. A. D. Jong and W. M. Spears....
[...]
...For the course of experiments we have used multi-modal problem generator (MPG) of Spears [6, 8]....
[...]
...For the course of experiments we have used multi-modal problem generator (MPG) of Spears [6, 8]....
[...]