Chaos Driven Evolutionary Algorithm: a New Approach for Evolutionary Optimization

This research deals with the initial investigations on the concept of a chaos-driven evolutionary algorithm Differential evolution. This paper is aimed at the embedding of simple two- dimensional chaotic system, which is Lozi map, in the form of chaos pseudo random number generator for Differential Evolution. The chaotic system of interest is the discrete dissipative system. Repeated simulations were performed on standard benchmark Schwefel's test function in higher dimensions. Finally, the obtained results are compared with canonical Differential Evolution. Keywords—Chaos, Differential evolution, Evolutionary algorithms, Lozi map. I. INTRODUCTION HESE days the methods based on soft computing such as neural networks, evolutionary algorithms, fuzzy logic, and genetic programming are known as powerful tool for almost any difficult and complex optimization problem. Ant Colony (ACO), Genetic Algorithms (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO) and Self Organizing Migration Algorithm (SOMA) are some of the most potent heuristics available. Recent studies have shown that Differential Evolution (1) has been used for a number of optimization tasks, (2), (3) has explored DE for combinatorial problems, (4) has hybridized DE whereas (5) - (7) has developed self-adaptive DE variants. This chapter is aimed at investigating the chaos driven DE. Although a several of papers have been recently focused on the connection of DE and chaotic dynamics either in the form of hybridizing of DE with chaotic searching algorithm (8) or in the form of chaotic mutation factor and dynamically changing weighting and crossover factor in self-adaptive

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