Leonardo Goliatt da Fonseca

Surrogate-assisted clonal selection algorithms for expensive optimization problems

Clonal selection algorithms are computational methods inspired by the behavior of the immune system which can be applied to solve optimization problems. However, like other nature inspired algorithms, they can require a large number of objective function evaluations in order to reach a satisfactory solution. When those evaluations involve a computationally expensive simulation model their cost becomes prohibitive. In this paper we analyze the use of surrogate models in order to enhance the performance of a clonal selection algorithm. Computational experiments are conducted to assess the performance of the presented techniques using a benchmark with 22 test-problems under a fixed budget of objective function evaluations. The comparisons show that for most cases the use of surrogate models improve significantly the performance of the baseline clonal selection algorithm.

A study on fitness inheritance for enhanced efficiency in real-coded genetic algorithms

This paper presents a study on the use of fitness inheritance as a surrogate model to assist a genetic algorithm (GA) in solving optimization problems with a limited computational budget.We compared the impact to the evolutionary search introducing three surrogate models: (i) averaged inheritance, (ii) weighted inheritance and (iii) parental inheritance. Numerical experiments are performed in order to assess the applicability and the performance of the proposed approach. The results show that when using a fixed reduced budget of expensive simulations, the surrogate-assisted genetic algorithm allows for improving the final solutions when compared to the standard GA. We find that the averaged and parental inheritance are more effective when compared to weighted inheritance, and they are recommended for expensive of optimization problems using GA-based search.