A General Analysis of Evolutionary Algorithms for Hard and Easy Fitness Functions

Details A General Analysis of Evolutionary Algorithms for Hard and Easy Fitness Functions A General Analysis of Evolutionary Algorithms for Hard and Easy Fitness Functions

2012-03-28

arxiv.org/abs/1203.6286v1

Computer Science

Neural and Evolutionary Computing

Scientific paper

This paper presents a general analysis of evolutionary algorithms for solving
hard and easy fitness functions. Two results are proven in the paper: (1) using
lower selection pressure is better for solving hard fitness functions; (2) the
strong cut-of point is 1 for solving any easy fitness function, which means it
brings no benefit if using a population size larger than 1.

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