Enhancing the Efficiency of The ECGA

Evolutionary Algorithms are largely used search and optimization procedures. They have been successfully applied for several problems and with proper care on the design process they can solve hard problems accurately, efficiently and reliably. The proper design of the algorithm turns some problems from intractable to tractable. We can go even further, using efficiency enhancements to turn them from tractable to practical. In this paper we show preliminary results of two efficiency enhancements proposed for Extended Compact Genetic Algorithm. First, a model building enhancement was used to reduce the complexity of the process from O(n 3) to O(n 2), speeding up the algorithm by 1000 times on a 4096 bits problem. Then, a local-search hybridization was used to reduce the population size by at least 32 times, reducing the memory and running time required by the algorithm. These results draw the first steps toward a competent and efficient Genetic Algorithm