Urban-Tissue Optimization through Evolutionary Computation

Evolutionary Computation Applied to Urban Traffic Optimization

Reinforcement Learning through Evolutionary Computation

This article characterizes the evolutionary algorithm approach to reinforcement learning in relation to the more standard, temporal diierence methods. We describe several research issues in reinforcement learning and discuss similarities and diierences in how they are addressed by the two methods. A short survey of evolutionary reinforcement learning systems and their successful applications is...

Multi-objective evolutionary computation and fuzzy optimization

In fuzzy optimization it is desirable that all fuzzy solutions under consideration be attainable, so that the decision maker will be able to make ‘‘a posteriori’’ decisions according to current decision environments. No additional optimization runs will be needed when the decision environment changes or when the decision maker needs to evaluate several decisions to establish the most appropriat...

Multi-Objective Optimization using Evolutionary Computation Techniques

In this paper an EP and PSO based optimization algorithms have been proposed for solving optimal power flow problems with multiple objective functions. These algorithms take into consideration all the equality and inequality constraints. The improvement in system performance is based on reduction in cost of power generation and active power loss. The

Hybrid Evolutionary Computation for Continuous Optimization

Hybrid optimization algorithms have gained popularity as it has become apparent there cannot be a universal optimization strategy which is globally more beneficial than any other. Despite their popularity, hybridization frameworks require more detailed categorization regarding: the nature of the problem domain, the constituent algorithms, the coupling schema and the intended area of application...