Practical optimization problems often have multiple objectives, which are likely to conflict with each other, and have more than one optimal... Show morePractical optimization problems often have multiple objectives, which are likely to conflict with each other, and have more than one optimal solution representing the best trade-offs among the competing objectives. Genetic algorithms, which optimize by repeatedly applying genetic operators to a population of possible solutions, have been used recently in multiobjective optimization, but often converge to a single solution that is not necessarily optimal due to lack of diversity in the population. Current multiobjective genetic and other evolutionary methods prevent this premature convergence by promoting new members that are dissimilar in parameter or objective space. A distance measure, which calculates similarities among the members in either objective or parameter space, is used to degrade the fitness of solutions when they are crowded in a small region. This process forces the algorithm to find new but distinct trade-off points in the objective or parameter space, but is computationally expensive. As the number of objectives or parameters increases, the methods fail to scale up and they deviate from the motivating concept of the genetic algorithm—natural evolution. We extend the standard genetic algorithm through two simple, yet powerful, changes motivated by natural evolution. In the first method, the algorithm, at each step, randomly or sequentially chooses one of the objectives for optimization; hence the method is called sequential extended genetic algorithm (SEGA). In the second method, a population is maintained for each objective, and crossover is performed selecting parents from across populations. This method is called parallel extended genetic algorithm (PEGA). We applied these methods to test problems from the literature, and to two well known problems, protein folding and multiple knapsack. We discovered our methods found better trade-off solutions than current multiobjective methods, without increasing computational complexity of genetic algorithms. PH.D in Computer Science, May 2013 Show less
