A unifying approach to heuristic search

Eiben, A.E.; Aarts, Ehl Emile; Hee, van Km Kees; Nuijten, Wpm Wim

doi:10.1007/bf02031717

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…There are existing frameworks for describing heuristic and evolutionary search techniques [16], [36] that mostly try to be as general as possible. Here we introduce a new one that is tailored for specifying csp solving eas.…”

Section: Describing Eas For Cspsmentioning

confidence: 99%

Comparing evolutionary algorithms on binary constraint satisfaction problems

Craenen

Eiben

Hemert

2003

IEEE Trans. Evol. Computat.

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Abstract-Constraint handling is not straightforward in evolutionary algorithms (ea) since the usual search operators, mutation and recombination, are 'blind' to constraints. Nevertheless, the issue is highly relevant, for many challenging problems involve constraints. Over the last decade numerous eas for solving constraint satisfaction problems (csp) have been introduced and studied on various problems. The diversity of approaches and the variety of problems used to study the resulting algorithms prevents a fair and accurate comparison of these algorithms. This paper aligns related work by presenting a concise overview and an extensive performance comparison of all these eas on a systematically generated test suite of random binary csps. The random problem instance generator is based on a theoretical model that fixes deficiencies of models and respective generators that have been formerly used in the Evolutionary Computing (ec) field.

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Section: Describing Eas For Cspsmentioning

confidence: 99%

Comparing evolutionary algorithms on binary constraint satisfaction problems

Craenen

Eiben

Hemert

2003

IEEE Trans. Evol. Computat.

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“…Particularly, most of the theoretical research efforts on unified framework and convergence theory have been only focused on the EAs, not on PBMH. For instance, Eiben et al (1995) presented a General Search Procedure (GSP) to provide a unifying framework for describing both constructive and iterative search algorithms, including GA, SA, threshold accepting, hill-climbing, depth-first search, breadth-first search, and best-first search. In order to specify such a general algorithm, seven elements were defined, i.e., search space, operations on set space, initial solution(s), selection, production, reduction and evaluation function.…”

Section: Introductionmentioning

confidence: 99%

A unified framework for population-based metaheuristics

Liu

Wang

2011

Ann Oper Res

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Based on the analysis of the basic schemes of a variety of population-based metaheuristics (PBMH), the main components of PBMH are described with functional relationships in this paper and a unified framework (UF) is proposed for PBMH to provide a comprehensive way of viewing PBMH and to help understand the essential philosophy of PBMH from a systematic standpoint. The relevance of the proposed UF and some typical PBMH methods is illustrated, including particle swarm optimization, differential evolution, scatter search, ant colony optimization, genetic algorithm, evolutionary programming, and evolution strategies, which can be viewed as the instances of the UF. In addition, as a platform to develop the new population-based metaheuristics, the UF is further explained to provide some designing issues for effective/efficient PBMH algorithms. Subsequently, the UF is extended, namely UFmeme to describe the Memetic Algorithms (MAs) by adding local search (memetic component) to the framework as an extra-feature. Finally, we theoretically

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Untitled

Labat¹,

Pomerol

2003

Journal of Heuristics

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A unifying approach to heuristic search

Cited by 3 publications

References 9 publications

Comparing evolutionary algorithms on binary constraint satisfaction problems

Comparing evolutionary algorithms on binary constraint satisfaction problems

A unified framework for population-based metaheuristics

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