Tutorial MCDM-T2 Evolutionary programming with diversity enhancement and ensemble strategies

The first part of the tutorials will cover various aspects of the evolutionary programming for real parameter optimization. We will introduce various mutation operators and various strategy parameter adaptation methods including the recently proposed adaptive evolutionary programming. We will also discuss hybridization of the evolutionary programming with other optimization paradigms. Over the last 4–5 decades, researchers have proposed several approaches / alternatives to construct evolutionary algorithms. Some such alternatives are Gaussian / Levy / Cauchy mutation operators, clearing / crowding / restricted tournament selection / sharing based niching algorithms, adaptive penalty / epsilon / superiority of feasible / stochastic ranking constraint handling approaches and so on. Clearly, there are several alternative approaches at every stage of constructing an evolutionary algorithm and users will have to perform numerous simulations to pick the best approaches and to fine tune parameters. This selection and subsequent parameter tuning approach is not efficient. In this tutorial, we will present an ensemble strategy with evolutionary programming to benefit from the need to tune parameters and availability of diverse approaches. Our research has shown the general applicability of the ensemble strategy in solving diverse problems. A common problem-faced by almost all evolutionary optimization algorithms is premature convergence when solving hard optimization problems. In order to tackle this problem, we describe our recently proposed strategies to enhance diversity while enhancing exploitation as well. We will also present a multi-objective evolutionary programming (MOEP) which treats each objective independently, but without using the computationally expensive non-domination sorting algorithm. This MOEP is up to 20 times faster compared to the same MOEP implementation with non-domination sorting algorithm.