Self-adaptation has been frequently employed in evolutionary computation. Angeline [1995] defines three distinct adaptive levels which are: population, individual, and component level. Cultural Algorithms have been shown to provide a framework in which to model self-adaptation at each of these levels. Here, we examine the role that different forms of knowledge can play in the self-adaptation pr...

This paper presents an approach to improving the performance of evolutionary algorithms. The evolutionary search e®ort is distributed among cooperating subpopulations that correspond to the substructures of the ̄tness landscape. The idea is to create such subpopulations that °ow easily on the simple substructures of the complex ̄tness landscape structure. We claim that the search on a complex ̄t...

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Although evolutionary algorithms are very different from other artificial intelligence search algorithms, they face similar fundamental issues — representation and search. There have been a large amount of work done in evolutionary computation on search, such as recombination operators, mutation operators, selection schemes and various specialised operators. In comparison, research in different...

Deepening our understanding of the characteristics and behaviors of population-based search algorithms remains an important ongoing challenge in Evolutionary Computation. To date however, most studies of Evolutionary Algorithms have only been able to take place within tightly restricted experimental conditions. For instance, many analytical methods can only be applied to canonical algorithmic f...

|Evolutionary 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 ou...

This paper approaches a recent hybrid evolutionary algorithm, called Evolutionary Clustering Search (ECS), that proposes a way of detecting promising areas combining an evolutionary algorithm and a iterative clustering. The search strategy become more aggressive in such detected areas by applying local search. In this paper, we developed a preprocessing phase for the ECS applying a locationallo...

This article introduces a generic sensitivity analysis method to measure the influence and interdependencies of Evolutionary Algorithms parameters. The proposed work focuses on its application to a Parallel Asynchronous Cellular Genetic Algorithm (PA-CGA). Experimental results on two different instances of a scheduling problem have demonstrated that some metaheuristic parameters values have lit...

Representation is widely recognised as a key determinant of performance in evolutionary computation. The development of families of representation-independentoperators allows the formulation of formal representation-independent evolutionary algorithms. These formal algorithms can be instantiated for particular search problems by selecting a suitable representation. The performance of different ...

In this paper, a hybrid evolutionary algorithm (HEA) is proposed to determine the optimal placement of multi-type flexible AC transmission system (FACTS) devices to simultaneously maximize the total transfer capability (TTC) and minimize the system real power loss of power transfers in deregulated power systems. Multi-objective optimal power flow (OPF) with FACTS devices including TTC, power lo...