Co-evolving Memetic Algorithms: Initial Investigations
نویسنده
چکیده
This paper presents and examines the behaviour of a system whereby the rules governing local search within a Memetic Algorithm are co-evolved alongside the problem representation. We describe the rationale for such a system, and the implementation of a simple version in which the evolving rules are encoded as (condition:action) patterns applied to the problem representation, and are e ectively self-adapted. We investigate the behaviour of the algorithm on a test suite of problems, and show signi cant performance improvements over a simple Genetic Algorithm, a Memetic Algorithm using a xed neighbourhood function, and a similar Memetic Algorithm which uses random rules, i.e. with the learning mechanism disabled. Analysis of these results enables us to draw some conclusions about the way that even the simpli ed system is able to discover and exploit di erent forms of structure and regularities within the problems. We suggest that this \meta-learning" of problem features provides a means both of creating highly scaleable algorithms, and of capturing features of the solution space in an understandable form.
منابع مشابه
Self-adaptative and Coevolving Memetic Algorithms
Results from applications of meta-heuristics, and Evolutionary Computation in particular, have led to the widespread acknowledgement of two facts. The first is that evolutionary optimisation can be improved by the use of local search methods, creating so-called Memetic Algorithms. The second is that there is no single ”best” choice of memetic operators and parametersrather the situation changes...
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