Combining Evolutionary And Non-evolutionary Methods In Tracking Dynamic Global Optima

Genetic Algorithms: Combining Evolutionary and `Non'-Evolutionary Methods in Tracking Dynamic Global Optima

The ability to track dynamic functional optima is important in many practical tasks. Recent research in this area has concentrated on modifying evolutionary algorithms (EAs) by triggering changes in control parameters, ensuring population diversity, or remembering past solutions. A set of results are presented that favourably compare hill climbing with a genetic algorithm, and reasons for the r...

OPTIMIZATION OF STEEL MOMENT FRAME BY A PROPOSED EVOLUTIONARY ALGORITHM

This paper presents an improved multi-objective evolutionary algorithm (IMOEA) for the design of planar steel frames. By considering constraints as a new objective function, single objective optimization problems turned to multi objective optimization problems. To increase efficiency of IMOEA different Crossover and Mutation are employed. Also to avoid local optima dynamic interference of mutat...

Tracking Optima in Dynamic Environments Using Evolutionary Algorithms - Report 3 Trung Thanh Nguyen September 2007

Optimization in Uncertain and Complex Dynamic Environments with Evolutionary Methods

In the real world, many of the optimization issues are dynamic, uncertain, and complex in which the objective function or constraints can be changed over time. Consequently, the optimum of these issues is changed nonlinearly. Therefore, the optimization algorithms not only should search the global optimum value in the space but also should follow the path of optimal change in dynamic environmen...

Free Search in Tracking Time Dependent Optima

The article presents an adaptive method, called Free Search. It implements ideas different from other evolutionary algorithms such as Genetic Algorithms, Particle Swarm Optimisation, Differential Evolution and Ant Colony Optimisation. Free Search is based on original concepts for individual intelligence and independence of the population members. It is applied to optimisation of time dependent ...