2012•Unpublished venueOpen access

Session details: Evolutionary computation software systems

Ştefan Wagner, Michael Affenzeller

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Abstract

The evolution of so many different metaheuristic optimization algorithms results from the fact that no single method can outperform all others for all possible problems. As postulated in the No Free Lunch Theorem, a general-purpose and universal optimization strategy is impossible. The only way how one strategy can outperform another is to be more specialized to the structure of the tackled problem. Consequently it always takes qualified algorithm experts to select and tune a metaheuristic algorithm for a concrete application.

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What this paper is about

The evolution of so many different metaheuristic optimization algorithms results from the fact that no single method can outperform all others for all possible problems. As postulated in the No Free Lunch Theorem, a general-purpose and universal optimization strategy is impossible. The only way how one strategy can outperform another is to be more specialized to the structure of the tackled problem. Consequently it always takes qualified algorithm experts to select and tune a metaheuristic algorithm for a concrete application.

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Available abstract

The evolution of so many different metaheuristic optimization algorithms results from the fact that no single method can outperform all others for all possible problems. As postulated in the No Free Lunch Theorem, a general-purpose and universal optimization strategy is impossible. The only way how one strategy can outperform another is to be more specialized to the structure of the tackled problem. Consequently it always takes qualified algorithm experts to select and tune a metaheuristic algorithm for a concrete application.

Key concepts: Metaheuristic, Computer science, Session (web analytics), Search-based software engineering, Evolutionary computation, Parallel metaheuristic, Computation, Evolution strategy

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