On the Easiest and Hardest Fitness Functions


The hardness of fitness functions is a crucial analysis topic in the sphere of evolutionary computation. In theory, this paper will help with understanding the flexibility of evolutionary algorithms (EAs). In observe, this paper could offer a tenet to the design of benchmarks. The aim of this paper is to answer the subsequent research questions. Given a fitness function class, that functions are the simplest with respect to an EA? That are the toughest? How are these functions made? This paper provides theoretical answers to those questions. The simplest and hardest fitness functions are created for an elitist (1 + 1) EA to maximise a category of fitness functions with the identical optima. It is demonstrated that the unimodal functions are the simplest and deceptive functions are the toughest in terms of the time-based mostly fitness landscape. This paper also reveals that in a very fitness perform category, the easiest function to one algorithm may become the toughest to a different algorithm, and vice versa.

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