Hindrances in the Fitness Landscape and Remedies to Achieve Optimization

Abstract

Past several decades have witnessed a rapid increase in the nature-inspired computational techniques. Evolutionary Computation is one such group of algorithms inspired by the theory of natural selection and survival of the fittest. This paper presents some for the key problems in the fitness landscape of such algorithms that make it difficult to converge to an optimum solution. These problems not only yield poor convergence but makes the use of Evolutionary Computation techniques less effective. This work then suggests some of the remedies to overcome these hindrances while designing the problem and the objective function. If properly incorporated, the suggested countermeasures enhance the ability of these methods in reaching an optimum solution faster and without entrapment in the local optima.