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Evolutionary computation: Comments on the history and current state
- IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
, 1997
"... Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950’s. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and ..."
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Cited by 280 (0 self)
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Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950’s. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA) [with links to genetic programming (GP) and classifier systems (CS)], evolution strategies (ES), and evolutionary programming (EP) by analysis and comparison of their most important constituents (i.e., representations, variation operators, reproduction, and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete.
Adaptive and self-adaptive evolutionary computation,” in Computational Intelligence: A Dynamic System Perspective
, 1995
"... This paper reviews the various studies that have introduced adaptive and self-adaptive parameters into Evolutionary Computations. A formal definition of an adaptive evolutionary computation is provided with an analysis of the types of adaptive and self-adaptive parameter update rules currently in us ..."
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Cited by 99 (3 self)
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This paper reviews the various studies that have introduced adaptive and self-adaptive parameters into Evolutionary Computations. A formal definition of an adaptive evolutionary computation is provided with an analysis of the types of adaptive and self-adaptive parameter update rules currently in use. Previous stud-ies are reviewed and placed into a categorization that helps to illustrate their simi-larities and differences.
Real-coded Memetic Algorithms with crossover hill-climbing
- Evolutionary Computation
, 2004
"... This paper presents a real-coded memetic algorithm that applies a crossover hillclimbing to solutions produced by the genetic operators. On the one hand, the memetic algorithm provides global search (reliability) by means of the promotion of high levels of population diversity. On the other, the cro ..."
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Cited by 71 (12 self)
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This paper presents a real-coded memetic algorithm that applies a crossover hillclimbing to solutions produced by the genetic operators. On the one hand, the memetic algorithm provides global search (reliability) by means of the promotion of high levels of population diversity. On the other, the crossover hill-climbing exploits the selfadaptive capacity of real-parameter crossover operators with the aim of producing an effective local tuning on the solutions (accuracy). An important aspect of the memetic algorithm proposed is that it adaptively assigns different local search probabilities to individuals. It was observed that the algorithm adjusts the global/local search balance according to the particularities of each problem instance. Experimental results show that, for a wide range of problems, the method we propose here consistently outperforms other real-coded memetic algorithms which appeared in the literature.
Adaptation in Evolutionary Computation: A Survey
- In Proceedings of the Fourth International Conference on Evolutionary Computation (ICEC 97
, 1997
"... Abstract � Adaptation of parameters and operators is one of the most important and promising areas of research in evolutionary computation � it tunes the algorithm to the problem while solving the problem. In this paper we develop a classi�cation of adaptation on the basis of the mechanisms used � a ..."
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Cited by 66 (6 self)
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Abstract � Adaptation of parameters and operators is one of the most important and promising areas of research in evolutionary computation � it tunes the algorithm to the problem while solving the problem. In this paper we develop a classi�cation of adaptation on the basis of the mechanisms used � and the level at which adaptation operates within the evolutionary algorithm. The classi�cation covers all forms of adaptation in evolutionary computation and suggests fur� ther research. I.
Adaptive operator selection with dynamic multiarmed bandits
- in Proc. 10th Ann. Conf. Genetic Evol. Comput. (GECCO), Atlanta, GA, 2008
"... An important step toward self-tuning Evolutionary Algorithms is to design efficient Adaptive Operator Selection procedures. Such a procedure is made of two main components: a credit assignment mechanism, that computes a reward for each operator at hand based on some characteristics of the past offsp ..."
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Cited by 26 (10 self)
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An important step toward self-tuning Evolutionary Algorithms is to design efficient Adaptive Operator Selection procedures. Such a procedure is made of two main components: a credit assignment mechanism, that computes a reward for each operator at hand based on some characteristics of the past offspring; and an adaptation rule, that modifies the selection mechanism based on the rewards of the different operators. This paper is concerned with the latter, and proposes a new approach for it based on the well-known Multi-Armed Bandit paradigm. However, because the basic Multi-Armed Bandit methods have been developed for static frameworks, a specific Dynamic Multi-Armed Bandit algorithm is proposed, that hybridizes an optimal Multi-Armed Bandit algorithm with the statistical Page-Hinkley test, which enforces the efficient detection of changes in time series. This original Operator Selection procedure is then compared to the state-of-the-art rules known as Probability Matching and Adaptive Pursuit on several artificial scenarios, after a careful sensitivity analysis of all methods. The Dynamic Multi-Armed Bandit method is found to outperform the other methods on a scenario from the literature, while on another scenario, the basic Multi-Armed Bandit performs best.
Self-Adaptive Genetic Algorithm for Numeric Functions
, 1996
"... Self-adaption is one of the most promising areas of research in evolutionary computation as it adapts the algorithm to the problem while solving the problem. In this paper we extend self-adaption to operate on more than one aspect of evolutionary computation and at more than one level of adaption. W ..."
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Cited by 22 (3 self)
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Self-adaption is one of the most promising areas of research in evolutionary computation as it adapts the algorithm to the problem while solving the problem. In this paper we extend self-adaption to operate on more than one aspect of evolutionary computation and at more than one level of adaption. We developed a genetic algorithm which self-adapts both mutation strength and population size; the results indicate that the approach works quite well. 1 Introduction Since evolutionary algorithms implement the idea of evolution, it is more than natural to expect some self-adapting characteristics of these techniques. Apart from evolutionary strategies, which incorporate some of its control parameters in the solution vectors, most other techniques use fixed representations, operators, and control parameters. Some of the promising research areas based on the inclusion of self adapting mechanisms are: ffl representation of individuals (as proposed by Shaefer (1987); the Dynamic Parameter Enco...
Crossover Improvement For The Genetic Algorithm In Information Retrieval
, 1998
"... Genetic algorithms (GAs) search for good solutions to a problem by operations inspired from the natural selection of living beings. Among their many uses, we can count information retrieval (IR). In this field, the aim of the GA is to help an IR system to find, in a huge documents text collection ..."
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Cited by 20 (2 self)
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Genetic algorithms (GAs) search for good solutions to a problem by operations inspired from the natural selection of living beings. Among their many uses, we can count information retrieval (IR). In this field, the aim of the GA is to help an IR system to find, in a huge documents text collection, a good reply to a query expressed by the user. The analysis of phenomena seen during the implementation of a GA for IR has brought us to a new crossover operation. This article introduces this new operation and compares it with other learning methods.
Self-Adaptation in Evolutionary Algorithms
- Parameter Setting in Evolutionary Algorithm
, 2006
"... this paper, we will give an overview over the self-adaptive behavior of evolutionary algorithms. We will start with a short overview over the historical development of adaptation mechanisms in evolutionary computation. In the following part, i.e., Section 2.2, we will introduce classification scheme ..."
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Cited by 19 (1 self)
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this paper, we will give an overview over the self-adaptive behavior of evolutionary algorithms. We will start with a short overview over the historical development of adaptation mechanisms in evolutionary computation. In the following part, i.e., Section 2.2, we will introduce classification schemes that are used to group the various approaches. Afterwards, self-adaptive mechanisms will be considered. The overview is started by some examples -- introducing self-adaptation of the strategy parameter and of the crossover operator. Several authors have pointed out that the concept of self-adaptation may be extended. Section 3.2 is devoted to such ideas. The mechanism of selfadaptation has been examined in various areas in order to find answers to the question under which conditions self-adaptation works and when it could fail. In the remaining sections, therefore, we present a short overview over some of the research done in this field
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.
