Function optimization by using genetic algorithms with individuals having different birth and survival rates

This paper proposes an effective approach to function optimisation using the concept of genetic algorithms. The proposed approach differs from the canonical genetic algorithm in that the populations of candidate solutions consist of individuals from various age-groups, and each individual is incorporated with an age attribute to enable its birth and survival rates to be governed by predefined ageing patterns. In order to ensure a stable search process, the condition that governs the relationships among the various birth and survival rates is determined. By generating the evolution of the populations with the genetic operators of selection, crossover and mutation, the proposed approach can provide excellent results by maintaining a better balance between exploitation and exploration of the solution space. A thorough study on the effects of the genetic parameters is carried out to examine the convergence behaviour of the proposed approach, and the findings illustrate how the convergence rate and the solution's quality are affected by the changes in the genetic parameters. The results of applying the proposed approach to solve five benchmark test problems are compared with those obtained by using the canonical genetic algorithm. Indeed, the proposed approach's performance is shown to surpass those of the canonical genetic algorithm.