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19
Tackling real-coded genetic algorithms: operators and tools for the behavioural analysis
- Arti Intelligence Reviews
, 1998
"... Abstract. Genetic algorithms play a significant role, as search techniques for handling com-plex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution prin ..."
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Cited by 189 (27 self)
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Abstract. Genetic algorithms play a significant role, as search techniques for handling com-plex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. These algorithms process a population of chromo-somes, which represent search space solutions, with three operations: selection, crossover and mutation. Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of real-coded genetic algorithms. Different models of genetic operators and some mechanisms available for studying the behaviour of this type of genetic algorithms are revised and compared. Key words: genetic algorithms, real coding, continuous search spaces Abbreviations: GAs – genetic algorithms; BCGA – binary-coded genetic algorithm; RCGA – real-coded genetic algorithm
Fuzzy Connectives Based Crossover Operators to Model Genetic Algorithms Population Diversity
, 1995
"... Genetic algorithms are adaptive methods which may be used to solve search and optimization problems. Genetic algorithms process a population of search space solutions with three operations: selection, crossover and mutation. An important problem in the use of genetic algorithms is the premature conv ..."
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Cited by 43 (25 self)
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Genetic algorithms are adaptive methods which may be used to solve search and optimization problems. Genetic algorithms process a population of search space solutions with three operations: selection, crossover and mutation. An important problem in the use of genetic algorithms is the premature convergence in a local optimum. Their main causes are the lack of diversity in the population and the disproportionate relationship between exploitation and exploration. The crossover operator is considered one of the most determinant elements for solving this problem. In this paper, we present new crossover operators based on fuzzy connectives for real-coded genetic algorithms. These operators are designed to avoid the premature convergence problem. To do so, they should keep the right exploitation/exploration balance to suitably model the diversity of the population.
Adaptation of Genetic Algorithm Parameters Based on Fuzzy Logic Controllers
- Genetic Algorithms and Soft Computing
"... . The genetic algorithm behaviour is determined by the exploitation and exploration relationship kept throughout the run. Adaptive genetic algorithms have been built for inducing exploitation/exploration relationships that avoid the premature convergence problem and improve the final results. One of ..."
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Cited by 37 (9 self)
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. The genetic algorithm behaviour is determined by the exploitation and exploration relationship kept throughout the run. Adaptive genetic algorithms have been built for inducing exploitation/exploration relationships that avoid the premature convergence problem and improve the final results. One of the most widely studied adaptive approaches are the adaptive parameter setting techniques. In this paper, we study these techniques in depth, based on the use of fuzzy logic controllers. Furthermore, we design and discuss an adaptive realcoded genetic algorithm based on the use of fuzzy logic controllers. Although suitable results have been obtained by using this type of adaptive technique, we report some reflections on open problems that still remain. Keywords. Exploitation/exploration relationship, adaptive genetic algorithms, fuzzy logic controllers. 1 Introduction GA behaviour is strongly determined by the balance between exploiting what already works best and exploring possibilities t...
Adaptive genetic operators based on coevolution with fuzzy behaviours
- IEEE TRANS ON EVOLUT COMPUT
, 2001
"... This paper presents a technique for adapting control parameter settings associated with genetic operators. Its principal features are: 1) the adaptation takes place at the individual level by means of fuzzy logic controllers (FLCs) and 2) the fuzzy rule bases used by the FLCs come from a separate g ..."
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Cited by 18 (2 self)
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This paper presents a technique for adapting control parameter settings associated with genetic operators. Its principal features are: 1) the adaptation takes place at the individual level by means of fuzzy logic controllers (FLCs) and 2) the fuzzy rule bases used by the FLCs come from a separate genetic algorithm (GA) that coevolves with the GA that applies the genetic operator to be controlled. The goal is to obtain fuzzy rule bases that produce suitable control parameter values for allowing the genetic operator to show an adequate performance on the particular problem to be solved. The empirical study of an instance of the technique has shown that it adapts the parameter settings according to the particularities of the search space allowing significant performance to be achieved for problems with different difficulties.
CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features
- Journal of Artificial Intelligence Research (JAIR
, 2005
"... In this paper we propose a crossover operator for evolutionary algorithms with real values that is based on the statistical theory of population distributions. The operator is based on the theoretical distribution of the values of the genes of the best individuals in the population. The proposed ope ..."
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Cited by 12 (2 self)
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In this paper we propose a crossover operator for evolutionary algorithms with real values that is based on the statistical theory of population distributions. The operator is based on the theoretical distribution of the values of the genes of the best individuals in the population. The proposed operator takes into account the localization and dispersion features of the best individuals of the population with the objective that these features would be inherited by the offspring. Our aim is the optimization of the balance between exploration and exploitation in the search process. In order to test the efficiency and robustness of this crossover, we have used a set of functions to be optimized with regard to different criteria, such as, multimodality, separability, regularity and epistasis. With this set of functions we can extract conclusions in function of the problem at hand. We analyze the results using ANOVA and multiple comparison statistical tests. As an example of how our crossover can be used to solve artificial intelligence problems, we have applied the proposed model to the problem of obtaining the weight of each network in a ensemble of neural networks. The results obtained are above the performance of standard methods. 1.
Fuzzy Genetic Algorithms: Issues and Models
- University of Granada
, 1999
"... There are two possible ways for integrating Fuzzy Logic and Genetic Algorithms. One involves the application of Genetic Algorithms for solving optimization and search problems related with fuzzy systems. The another, the use of fuzzy tools and Fuzzy Logic-based techniques for modeling different Gene ..."
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Cited by 8 (0 self)
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There are two possible ways for integrating Fuzzy Logic and Genetic Algorithms. One involves the application of Genetic Algorithms for solving optimization and search problems related with fuzzy systems. The another, the use of fuzzy tools and Fuzzy Logic-based techniques for modeling different Genetic Algorithm components and adapting Genetic Algorithm control parameters, with the goal of improving performance. The Genetic Algorithms resulting from this integration are called Fuzzy Genetic Algorithms. In this contribution, we tackle Fuzzy Genetic Algorithms by analyzing their definition based on the Zadeh's concept of Fuzzy Algorithms and the two different meanings as Fuzzy Logic may be viewed. We review different approaches, attempt to identify some open issues and summarize a few new promising research directions on the topic. Keywords: Fuzzy Logic, Genetic Algorithms, Fuzzy Algorithms, Fuzzy Genetic Algorithms. This research has been supported by CICYT TIC96-0778. 1 Introductio...
Verdegay. The use of fuzzy connectives to design real-coded genetic algorithms
- Mathware & Soft Computing
, 1995
"... Genetic algorithms are adaptive methods that use principles inspired by natural population genetics to evolve solutions to search and optimization problems. Genetic algorithms process a population of search space solutions with three operations: selection, crossover and mutation. A great problem in ..."
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Cited by 6 (2 self)
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Genetic algorithms are adaptive methods that use principles inspired by natural population genetics to evolve solutions to search and optimization problems. Genetic algorithms process a population of search space solutions with three operations: selection, crossover and mutation. A great problem in the use of genetic algorithms is premature convergence; the search becomes trapped in a local optimum before the global optimum is found. Fuzzy logic techniques may be used for solving this problem. This paper presents one of them: the design of crossover operators for real-coded genetic algorithms using fuzzy connectives and its extension based on the use of parameterized fuzzy connectives as tools for tackling the premature convergence problem.
Dynamic and Heuristic Fuzzy Connectives Based Crossover Operators for Controlling the Diversity and Convergence of Real-Coded Genetic Algorithms
- International Journal of Intelligent Systems
, 1996
"... Genetic algorithms are adaptive methods which may be used as approximation heuristic for search and optimization problems. Genetic algorithms process a population of search space solutions with three operations: selection, crossover and mutation. A great problem in the use of genetic algorithms is t ..."
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Cited by 6 (5 self)
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Genetic algorithms are adaptive methods which may be used as approximation heuristic for search and optimization problems. Genetic algorithms process a population of search space solutions with three operations: selection, crossover and mutation. A great problem in the use of genetic algorithms is the premature convergence, a premature stagnation of the search caused by the lack of diversity in the population and a disproportionate relationship between exploitation and exploration. The crossover operator is considered one of the most determinant elements for solving this problem. In this paper we present two types of crossover operators based on fuzzy connectives for real-coded genetic algorithms. The first type is designed to keep a suitable sequence between the exploration and the exploitation along the GA's run, the dynamic fuzzy connectives-based crossover operators, the second, for generating offspring near to the best parents in order to offer diversity or convergence in a profit...
A Fuzzy-Based Lifetime Extension of Genetic Algorithms
- Fuzzy Sets and Systems
, 2005
"... In knowledge discovery, Genetic Algorithms have been used for classification, model selection, and other optimization tasks. However, behavior and performance of genetic algorithms are directly affected by the values of their input parameters, while poor parameter settings usually lead to several pr ..."
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Cited by 3 (1 self)
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In knowledge discovery, Genetic Algorithms have been used for classification, model selection, and other optimization tasks. However, behavior and performance of genetic algorithms are directly affected by the values of their input parameters, while poor parameter settings usually lead to several problems such as the premature convergence. Adaptive techniques have been suggested for adjusting the parameters in the process of running the genetic algorithm. None of these techniques have yet shown a significant overall improvement, since most of them remain domain-specific. In this paper, we attempt to improve the performance of genetic algorithms by providing a new, fuzzy-based extension of the LifeTime feature. We use a Fuzzy Logic Controller (FLC) to adapt the crossover probability as a function of the chromosomes ’ age. The general principle is that for both young and old individuals the crossover probability is naturally low, while there is a certain age interval, where this probability is high. The concepts of “young”, “old”, and “middle-aged ” are modeled as linguistic variables. This approach should enhance the exploration and exploitation capabilities of the algorithm, while reducing its rate of premature convergence. We have evaluated the proposed Lifetime methodology on several benchmark problems by comparing its performance to the basic genetic algorithm and to several adaptive genetic algorithms. The results of our initial experiments demonstrate a clear advantage of the fuzzy-based Lifetime extension over the “crisp ” techniques.
Adaptive Genetic Algorithms Based on Coevolution with Fuzzy Behaviors
- DEPT. OF COMPUTER SCIENCE AND A. I., UNIVERSITY OF GRANADA, SPAIN. (AVAILABLE
, 1998
"... Adaptive genetic algorithms dynamically adjust the genetic algorithm configuration during the course of evolving a problem solution in order to offer an appropriate balance between exploration (overall search in the solution space) and exploitation (localized search in the promising regions discover ..."
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Cited by 2 (1 self)
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Adaptive genetic algorithms dynamically adjust the genetic algorithm configuration during the course of evolving a problem solution in order to offer an appropriate balance between exploration (overall search in the solution space) and exploitation (localized search in the promising regions discovered in that space). One promising way followed for building adaptive genetic algorithms involves the application of fuzzy logic controllers for tuning genetic algorithm control parameters. In this paper, a general model based on fuzzy logic controllers is presented for adapting parameters that control the application of any genetic operator. Our proposal is called coevolution with fuzzy behaviors. A fuzzy behavior is a vector with the linguistic values of the fuzzy rule consequent of a fuzzy logic controller, that encodes its fuzzy rule base. Control parameter values are computed for each set of parents that undergo the genetic operator, using a fuzzy logic controller that considers particula...
