Authors:
Simon Reichhuber
and
Sven Tomforde
Affiliation:
Intelligent Systems, University of Kiel, Hermann-Rodewald-Str. 3, Kiel, Germany
Keyword(s):
Evolutionary Computation, Genetic Algorithms, Optimisation, Fitness Landscapes, Diversity, Bet-based Approach.
Abstract:
Evolutionary Algorithms (EA) are a well-studied field in nature-inspired optimisation. Their success over the last decades has led to a large number of extensions, which are particularly suitable for certain characteristics of specific problems. Alternatively, variants of the basic approach have been proposed, for example to increase efficiency. In this paper, we focus on the latter: We propose to enrich the evolutionary problem with a self- controlling betting strategy to optimise the evolution of individuals over successive generations. For this purpose, each individual is given a betting parameter to be co-optimised, which allows him to improve his chances of “survival” by betting. We analyse the behaviour of our approach compared to standard procedures by using a reference set of complex functional problems.