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Tackling realcoded 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 complex 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 142 (25 self)
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Abstract. Genetic algorithms play a significant role, as search techniques for handling complex 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 chromosomes, 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 realcoded 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 – binarycoded genetic algorithm; RCGA – realcoded genetic algorithm
CaseBased Initialization of Genetic Algorithms
, 1993
"... In this paper, we introduce a casebased method of initializing genetic algorithms that are used to guide search in changing environments. This is incorporated in an anytime learning system. Anytime learning is a general approach to continuous learning in a changing environment. The agent's lea ..."
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Cited by 72 (6 self)
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In this paper, we introduce a casebased method of initializing genetic algorithms that are used to guide search in changing environments. This is incorporated in an anytime learning system. Anytime learning is a general approach to continuous learning in a changing environment. The agent's learning module continuously tests new strategies against a simulation model of the task environment, and dynamically updates the knowledge base used by the agent on the basis of the results. The execution module includes a monitor that can dynamically modify the simulation model based on its observations of the external environment; an update to the simulation model causes the learning system to restart learning. Previous work has shown that genetic algorithms provide an appropriate search mechanism for anytime learning. This paper extends the approach by including strategies, which are learned under similar environmental conditions, in the initial population of the genetic algorithm. Experiments s...
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 31 (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...
Alternative random initialization in genetic algorithms
 Proceedings of the 7 th International Conference on Genetic Algorithms
, 1997
"... Though unanimously recognized as a crucial step in Evolutionary Algorithms, initialization procedures have not been paid much attention so far. In bitstring Genetic Algorithms, for instance, the standard 0/1 equiprobable choice for every bit is rarely discussed, as the resulting distribution probabi ..."
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Cited by 16 (11 self)
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Though unanimously recognized as a crucial step in Evolutionary Algorithms, initialization procedures have not been paid much attention so far. In bitstring Genetic Algorithms, for instance, the standard 0/1 equiprobable choice for every bit is rarely discussed, as the resulting distribution probability over the whole bitstring space is uniform. However, uniformity is relative to a measure on the search space. First, considering the measure given by the density of 1's, the Uniform Covering initialization procedure is naturally designed. Second, taking into account the probability of appearance of sequences of identical bits leads to design another alternative initialization procedure, the Homogeneous Block procedure. These procedures are compared with the standard initialization procedure on several problems. A priori comparison is achieved using FitnessDistance Correlation. Actual experiments demonstrate the accuracy of these FDCbased comparisons, and emphasize the usefulness of the two proposed procedure. 1
Modeling GA Performance for Control Parameter Optimization
 GECCO2000: Proceedings of the Genetic and Evolutionary Computation Conference
, 2000
"... Optimization of the control parameters of genetic algorithms is often a time consuming and tedious task. In this work we take the metalevel genetic algorithm approach to control parameter optimization. We enhance this process by incorporating a neural network for fitness evaluation. This neur ..."
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Cited by 15 (5 self)
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Optimization of the control parameters of genetic algorithms is often a time consuming and tedious task. In this work we take the metalevel genetic algorithm approach to control parameter optimization. We enhance this process by incorporating a neural network for fitness evaluation. This neural network is trained to learn the complex interactions of the genetic algorithm control parameters and is used to predict the performance of the genetic algorithm relative to values of these control parameters. To validate our approach we describe a genetic algorithm for the largest common subgraph problem that we develop using this neural network enhanced metalevel genetic algorithm. The resulting genetic algorithm significantly outperforms a handtuned variant and is shown to be competitive with a hillclimbing algorithm used in practical applications. 1 Introduction Genetic algorithms use a number of parameters to control their evolutionary search for the solution to thei...
A Prescriptive Formalism for Constructing Domainspecific Evolutionary Algorithms
, 1998
"... It has been widely recognised in the computational intelligence and machine learning communities that the key to understanding the behaviour of learning algorithms is to understand what representation is employed to capture and manipulate knowledge acquired during the learning process. However, trad ..."
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Cited by 13 (0 self)
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It has been widely recognised in the computational intelligence and machine learning communities that the key to understanding the behaviour of learning algorithms is to understand what representation is employed to capture and manipulate knowledge acquired during the learning process. However, traditional evolutionary algorithms have tended to employ a fixed representation space (binary strings), in order to allow the use of standardised genetic operators. This approach leads to complications for many problem domains, as it forces a somewhat artificial mapping between the problem variables and the canonical binary representation, especially when there are dependencies between problem variables (e.g. problems naturally defined over permutations). This often obscures the relationship between genetic structure and problem features, making it difficult to understand the actions of the standard genetic operators with reference to problemspecific structures. This thesis instead advocates m...
Boosting Stochastic Problem Solvers through Online SelfAnalysis of Performance
, 2003
"... In many combinatorial domains, simple stochastic algorithms often exhibit superior performance when compared to highly customized approaches. Many of these simple algorithms outperform more sophisticated approaches on difficult benchmark problems; and often lead to better solutions as the algorithms ..."
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Cited by 9 (3 self)
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In many combinatorial domains, simple stochastic algorithms often exhibit superior performance when compared to highly customized approaches. Many of these simple algorithms outperform more sophisticated approaches on difficult benchmark problems; and often lead to better solutions as the algorithms are taken out of the world of benchmarks and into the realworld. Simple stochastic algorithms are often robust, scalable problem solvers.
Understanding evolutionary computing: A hands on approach
 In IEEE International Conference on Evolutionary Computation
, 1998
"... AbstractEvolutionary computing is the study of robust search algorithms based on the principles of evolution. An Evolutionary Algorithm (EA) searches a problem space in order to nd regions containing good solutions. Typically EA users judge the quality of their algorithms by the quality of the solu ..."
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Cited by 9 (1 self)
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AbstractEvolutionary computing is the study of robust search algorithms based on the principles of evolution. An Evolutionary Algorithm (EA) searches a problem space in order to nd regions containing good solutions. Typically EA users judge the quality of their algorithms by the quality of the solutions found. This approach ignores the behavior of the search algorithm and concentrates solely on the outcome. As a result, the user is unaware of their algorithm's actions or how the solutions were discovered. This paper describes how search space visualizations can be used to facilitate the user's understanding of evolutionary computing. A set of examples are presented showing how the user can take a \hands on " approach to explore the behavior of their algorithms and interact with the evolutionary search process.
Empirical Modeling of Genetic Algorithms
 EVOLUTIONARY COMPUTATION
, 2001
"... This paper addresses the problem of reliably setting genetic algorithm parameters for consistent labelling problems. Genetic algorithm parameters are notoriously difficult to determine. This paper proposes a robust empirical framework, based on the analysis of factorial experiments. The use of a gra ..."
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Cited by 7 (1 self)
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This paper addresses the problem of reliably setting genetic algorithm parameters for consistent labelling problems. Genetic algorithm parameters are notoriously difficult to determine. This paper proposes a robust empirical framework, based on the analysis of factorial experiments. The use of a graecolatin square permits an initial study of a wide range of parameter settings. This is followed by fully crossed factorial experiments with narrower ranges, which allow detailed analysis by logistic regression. The empirical models thus derived can be used first to determine optimal algorithm parameters, and second to shed light on interactions between the parameters and their relative importance. The initial models do not extrapolate well. However, an advantage of this approach is that the modelling process is under the control of the experimenter, and is hence very flexible. Refined models are produced, which are shown to be robust under extrapolation to up to triple the problem size.
Dynamic and Heuristic Fuzzy Connectives Based Crossover Operators for Controlling the Diversity and Convergence of RealCoded 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 realcoded 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 connectivesbased crossover operators, the second, for generating offspring near to the best parents in order to offer diversity or convergence in a profit...