Self-adaptation of mutation distribution in evolution strategies for dynamic optimization problems

Copyright @ IOS Press. All Rights Reserved.Evolution strategies with q-Gaussian mutation, which allows the self-adaptation of the mutation distribution shape, is proposed for dynamic optimization problems in this paper. In the proposed method, a real parameter q, which allows to smoothly control the shape of the mutation distribution, is encoded in the chromosome of the individuals and is allowed to evolve. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutation on experiments generated from the simulation of evolutionary robots and on dynamic optimization problems generated by the Moving Peaks generator

Dynamic Optimization in Two-Party Models

The goal of this paper is to study the problem of optimal dynamic policy formulation with competing political parties. We study a general class of problems, in which the two competing political parties have quadratic intertemporal objective functions, and in which the economy has a linear structure and a multidimensional state space. For the general linear quadratic problem we develop a numerical dynamic programming algorithm to solve for optimal policies of each party taking into account the party's objectives; the structure of the economy ; the probability of future election results; and the objectives of the other political party.

Dynamic optimization in natural resources management

Dynamic modeling is general and recently the most interesting perspective to solve a dynamic economic problem based on Pontryagin’s maximum principle. Moreover traditional economic theory, up to the middle of twentieth century, builds up the production functions regardless the inputs’ scarcity. Nowadays it is clear that both the inputs are depletable quantities and a lot of constraints are imposed in their usage in order to ensure economic sustainability. For example the input “oil” used in the production is a non renewable resource so it can be exhausted. In a same way every biomass resides in ecosystems is a resource that can be used in a generalized production function for capital accumulation purposes but the latter resource is a renewable one. The purpose of this paper is the presentation of some natural resources dynamic models in order to extract the optimal trajectories of the state and control variables for the optimal control economic problem. We show how methods of infinite horizon optimal control theory developed for natural resources models.Dynamic optimization; optimal control; maximum principle; natural resources

Evolutionary algorithms for dynamic optimization problems: workshop preface

Copyright @ 2005 AC

Learning in abstract memory schemes for dynamic optimization

We investigate an abstraction based memory scheme for evolutionary algorithms in dynamic environments. In this scheme, the abstraction of good solutions (i.e., their approximate location in the search space) is stored in the memory instead of good solutions themselves and is employed to improve future problem solving. In particular, this paper shows how learning takes place in the abstract memory scheme and how the performance in problem solving changes over time for different kinds of dynamics in the fitness landscape. The experiments show that the abstract memory enables learning processes and efficiently improves the performance of evolutionary algorithms in dynamic environments