Adam-Eve genetic algorithm as a function optimizer

There are several different types of evolutionary algorithm (EA). Genetic algorithms (GAs) receive much attention as general-purpose methods for search and optimization. GAs which are powerful stochastic optimization techniques are perhaps the most widely known type of EAs. There are several methods to make GAs faster and our proposed Adam-Eve-like GA appears to be a promising methodology. Taking a hint from nature, the proposed method has some special characteristics which distinguish it from others. The Adam-Eve-like GA is discussed and its performance is compared with a traditional GA. Standard online and offline performance were evaluated and the obtained results were compared with those from a traditional GA. The basic idea of GAs has been taken from nature, therefore it is reasonable to expect an improvement in our new model for GA, because it is much closer to the evolution of organisms. Results provide valuable evidence that the proposed Adam-Eve-like GA outperforms the traditional GA in both online and offline performance measures. Based on the results from an optimization standpoint, the Adam-Eve-like GA seems to produce reasonable engineering results in an inherently difficult area where few other techniques are available. Furthermore they suggest that it may be beneficial to apply the algorithm on the real-world difficult optimization problems.