Using Genetic Algorithms to Search for an Optimal Solution to the Schaffer f6 Function

Using Genetic Algorithms to Search for an Optimal Solution to the Schaffer f6 FunctionFour different genetic algorithms are used to search for optimal solutions to the Schaffer f6 function; a simple GA, a GA using adaptive mutation, a steady state GA, and a GA using an island model. The methods are compared in terms of their reliability and efficiency.

Solutions to complex optimization problems can often times be found by making use of genetic algorithms. A genetic algorithm (GA) is a tool which is modeled after Darwin’’s principle of natural selection. As a population of candidate solutions is “evolved”, by means of selection, crossover and mutation, it generally tends to approach the global optimum solution regardless of the terrain of the fitness landscape. Multi-dimensional functions make for good GA test cases because of their nonlinearity and oscillation around the optimal solutions. We examined one such function, commonly referred to as the Schaffer f6 function. We attempted to optimize this function using a simple GA and then compared our results to those obtained by implementing 3 new features into the GA. We concluded that both the island model and the steady state algorithm were the most reliable, however, the steady state algorithm was much more efficient.
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