A new generationless parallel evolutionary algorithm for combinatorial optimization

This paper presents a new parallel evolutionary approach where the concept of generation has been removed and replaced by the cycle one. Indeed, the classical genetic algorithms (GAs) deals with operations on the whole population through all generations. These operations are performed during the evolution towards the best individual or solution of the considered combinatorial problem. In our approach, each individual participates to the evolutionary process uniquely during some iterations. There is no generation where all individuals are created at the same time and disappear at the same time at the end of the evolutionary process genation. In our approach, each individual owns one lifespan represented by a number of cycles which are affected to it randomly at its birth and at the end of which it disappears from the population. Consequently, only certain individuals of the population are evaluated within each iteration of the algorithm and not all the population. This causes the substantial reduction of the total running time of the algorithm since the evaluations of all individuals of each generation necessitates more than 80% of the total running time of a classical GA. This approach has been developed with the goal to present a new and efficient parallel scheme of the classical GA with better performances in terms of running time. In this paper, we will present a new asynchronous parallel Master/Slave scheme of the GA and will show the power of our approach with the classification extraction rules problem.