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116
Multiobjective Evolutionary Algorithms: Analyzing the StateoftheArt
, 2000
"... Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mideighties in an attempt to stochastically solve problems of this generic class. During the past decade, ..."
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Cited by 424 (7 self)
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Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mideighties in an attempt to stochastically solve problems of this generic class. During the past decade, a variety of multiobjective EA (MOEA) techniques have been proposed and applied to many scientific and engineering applications. Our discussion's intent is to rigorously define multiobjective optimization problems and certain related concepts, present an MOEA classification scheme, and evaluate the variety of contemporary MOEAs. Current MOEA theoretical developments are evaluated; specific topics addressed include fitness functions, Pareto ranking, niching, fitness sharing, mating restriction, and secondary populations. Since the development and application of MOEAs is a dynamic and rapidly growing activity, we focus on key analytical insights based upon critical MOEA evaluation of c...
Evolutionary Algorithms for Engineering Applications
, 1997
"... This paper focuses on the issue of evaluation of constraints handling methods, as the advantages and disadvantages of various methods are not well understood. The general way of dealing with constraints  whatever the optimization method  is by penalizing infeasible points. However, there are no ..."
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Cited by 71 (3 self)
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This paper focuses on the issue of evaluation of constraints handling methods, as the advantages and disadvantages of various methods are not well understood. The general way of dealing with constraints  whatever the optimization method  is by penalizing infeasible points. However, there are no guidelines on designing penalty functions. Some suggestions for evolutionary algorithms are given in [37], but they do not generalize. Other techniques that can be used to handle constraints in are more or less problem dependent. For instance, the knowledge about linear constraints can be incorporated into specific operators [24], or a repair operator can be designed that projects infeasible points onto feasible ones [30]
Realcoded Memetic Algorithms with crossover hillclimbing
 Evolutionary Computation
, 2004
"... This paper presents a realcoded 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 70 (12 self)
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This paper presents a realcoded 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 hillclimbing exploits the selfadaptive capacity of realparameter 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 realcoded memetic algorithms which appeared in the literature.
An Overview of Evolutionary Algorithms: Practical Issues and Common Pitfalls
 Information and Software Technology
, 2001
"... An overview of evolutionary algorithms is presented covering genetic algorithms, evolution strategies, genetic programming and evolutionary programming. The schema theorem is reviewed and critiqued. Gray codes, bit representations and realvalued representations are discussed for parameter optimi ..."
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Cited by 57 (0 self)
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An overview of evolutionary algorithms is presented covering genetic algorithms, evolution strategies, genetic programming and evolutionary programming. The schema theorem is reviewed and critiqued. Gray codes, bit representations and realvalued representations are discussed for parameter optimization problems. Parallel Island models are also reviewed, and the evaluation of evolutionary algorithms is discussed.
The Island Model Genetic Algorithm: On Separability, Population Size and Convergence
 Journal of Computing and Information Technology
, 1998
"... Parallel Genetic Algorithms have often been reported to yield better performance than Genetic Algorithms which use a single large panmictic population. In the case of the Island Model genetic algorithm, it has been informally argued that having multiple subpopulations helps to preserve genetic di ..."
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Cited by 56 (0 self)
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Parallel Genetic Algorithms have often been reported to yield better performance than Genetic Algorithms which use a single large panmictic population. In the case of the Island Model genetic algorithm, it has been informally argued that having multiple subpopulations helps to preserve genetic diversity, since each island can potentially follow a different search trajectory through the search space. It is also possible that since linearly separable problems are often used to test Genetic Algorithms, that Island Models may simply be particularly well suited to exploiting the separable nature of the test problems. We explore this possibility by using the infinite population models of simple genetic algorithms to study how Island Models can track multiple search trajectories. We also introduce a simple model for better understanding when Island Model genetic algorithms may have an advantage when processing some test problems. We provide empirical results for both linearly separa...
The exploration/exploitation tradeoff in dynamic cellular genetic algorithms
 IEEE Transactions on Evolutionary Computation
, 2005
"... Abstract—This paper studies static and dynamic decentralized versions of the search model known as cellular genetic algorithm (cGA), in which individuals are located in a specific topology and interact only with their neighbors. Making changes in the shape of such topology or in the neighborhood may ..."
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Cited by 47 (8 self)
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Abstract—This paper studies static and dynamic decentralized versions of the search model known as cellular genetic algorithm (cGA), in which individuals are located in a specific topology and interact only with their neighbors. Making changes in the shape of such topology or in the neighborhood may give birth to a high number of algorithmic variants. We perform these changes in a methodological way by tuning the concept of ratio. Since the relationship (ratio) between the topology and the neighborhood shape defines the search selection pressure, we propose to analyze in depth the influence of this ratio on the exploration/exploitation tradeoff. As we will see, it is difficult to decide which ratio is best suited for a given problem. Therefore, we introduce a preprogrammed change of this ratio during the evolution as a possible additional improvement that removes the need of specifying a single ratio. A later refinement will lead us to the first adaptive dynamic kind of cellular models to our knowledge. We conclude that these dynamic cGAs have the most desirable behavior among all the evaluated ones in terms of efficiency and accuracy; we validate our results on a set of seven different problems of considerable complexity in order to better sustain our conclusions. Index Terms—Cellular genetic algorithm (cGA), evolutionary algorithm (EA), dynamic adaptation, neighborhoodtopopulation ratio. I.
Statistical Racing Techniques for Improved Empirical Evaluation of Evolutionary Algorithms
 Parallel Problem Solving from Nature  PPSN VIII, 8th International Conference
, 2004
"... Abstract. In empirical studies of Evolutionary Algorithms, it is usually desirable to evaluate and compare algorithms using as many different parameter settings and test problems as possible, in order to have a clear and detailed picture of their performance. Unfortunately, the total number of exper ..."
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Cited by 30 (4 self)
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Abstract. In empirical studies of Evolutionary Algorithms, it is usually desirable to evaluate and compare algorithms using as many different parameter settings and test problems as possible, in order to have a clear and detailed picture of their performance. Unfortunately, the total number of experiments required may be very large, which often makes such research work computationally prohibitive. In this paper, the application of a statistical method called racing is proposed as a generalpurpose tool to reduce the computational requirements of largescale experimental studies in evolutionary algorithms. Experimental results are presented that show that racing typically requires only a small fraction of the cost of an exhaustive experimental study. 1
Island Model Genetic Algorithms and Linearly Separable Problems
 In Evolutionary Computing, AISB Workshop
, 1997
"... Parallel Genetic Algorithms have often been reported to yield better performance than Genetic Algorithms which use a single large panmictic population. In the case of the Island Model Genetic Algorithm, it has been informally argued that having multiple subpopulations helps to preserve genetic diver ..."
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Cited by 24 (0 self)
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Parallel Genetic Algorithms have often been reported to yield better performance than Genetic Algorithms which use a single large panmictic population. In the case of the Island Model Genetic Algorithm, it has been informally argued that having multiple subpopulations helps to preserve genetic diversity, since each island can potentially follow a different search trajectory through the search space. On the other hand, linearly separable functions have often been used to test Island Model Genetic Algorithms
Representation Issues in Neighborhood Search and Evolutionary Algorithms
, 1998
"... this paper we explore some very general properties of representations as they relate to neighborhood search methods. In particular, we looked at the expected number of local optima under a neighborhood search operator when averaged overall possible representations. The number of local optima under a ..."
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Cited by 21 (3 self)
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this paper we explore some very general properties of representations as they relate to neighborhood search methods. In particular, we looked at the expected number of local optima under a neighborhood search operator when averaged overall possible representations. The number of local optima under a neighborhood search operator for standard Binary and standard binary reflected Gray codes is developed and explored as one measure of problem complexity. We also relate number of local optima to another metric, OE, designed to provide one measure of complexity with respect to a simple genetic algorithm