Evolutionary algorithms are general iterative algorithms for combinatorial optimization. The term evolutionary algorithm is used to refer to any probabilistic algorithm whose design is inspired by evolutionary mechanisms found in biological species. These algorithms have been found to be very effective and robust in solving numerous problems from a wide range of application domains. In this paper we perform a comparative study among Genetic Algorithms (GA), Simulated Annealing (SA), Differential Evolution (DE), and Self Organising Migrating Algorithms (SOMA). These algorithms have many similarities, but they also possess distinctive features, mainly in their strategies for searching the solution state space. The four heuristics are applied on the same optimization problem Travelling Salesman Problem (TSP) and compared with respect to (1) quality of the best solution identified by each heuristic, (2) progress of the search from an initial solution until stopping criteria are met. INTRODUCTION TO EVOLUTIONARY ALGORITHMS Evolutionary algorithms (EAs) have many interesting properties and have been widely used in various optimization problems from combinatorial problems such as job shop scheduling to real valued parameter optimization (Back et al. 1997). In computer science, evolutionary computation is a subfield of artificial intelligence (more particularly computational intelligence) that involves combinatorial optimization problems. Evolutionary computation uses iterative progress, such as growth or development in a population. This population is then selected in a guided random search using parallel processing to achieve the desired end. Such processes are often inspired by biological mechanisms of evolution. As evolution can produce highly optimised processes and networks, it has many applications in computer science. Problem solution using evolutionary algorithms is shown in Figure 1. Figure 1: Problem solution using evolutionary algorithms (adapted from http://jpmc.sourceforge.net )
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