Evolutionary Multi Clause Samples

Evolutionary Multi. Objective Optimization‌ Evolutionary multi-objective optimization (EMO) algorithms [88] use a population-based approach in which more than one solution participates in an iteration and evolves a new population of solutions in each iteration. Such class of algorithms lies within the class of genetic algorithms (GA). Evolutionary multi-objective optimization algorithms are a good choice in solving multi-objective optimization problems because EMO problems, by nature, give rise to a set of Pareto-optimal solutions. Generally speaking, EMO algorithms can be all considered as structured as a main loop where the first population P (of solutions) is usually created at random, say P = {Γ1, Γ2, . . . , ΓN }. At each iteration, a new population is generated by using four main operations: (i) selection, (ii) crossover, (iii) mutation, and