Providing an appropriate search space to solve the fatigue problem in interactive evolutionary computation

2005 ◽  
Vol 23 (2) ◽  
pp. 115-127 ◽  
Author(s):  
Fang -Cheng Hsu ◽  
Peter Huang
Keyword(s):  
Evolutionary Computation ◽  
Search Space ◽  
Interactive Evolutionary Computation

Users' Fuzzy Cognition Knowledge Learning in Interactive Evolutionary Computation and its Application

2011 ◽  
Vol 204-210 ◽  
pp. 245-250
Author(s):  
Guo Sheng Hao ◽  
Xiang Jun Zhao ◽  
Yong Qing Huang
Keyword(s):  
Evolutionary Computation ◽  
Search Space ◽  
Interactive Evolutionary Computation ◽  
Sense Unit ◽  
Knowledge Learning

user in interactive evolutionary computation (IEC) has the characteristic of fuzzy cognition. Based on this, a method to learn users’ fuzzy cognition knowledge is given. The method includes the fuzzy expression of the basic elements of IEC such as search space, population, gene sense unit and so on. Then a method to increase the performance of IEC based on the knowledge of users’ fuzzy cognition is given. The above results enrich the researches of IEC users' cognition.

Efficient Evolution of Modular Robot Control via Genetic Programming

2013 ◽  
pp. 59-85
Author(s):  
Tüze Kuyucu ◽  
Ivan Tanev ◽  
Katsunori Shimohara
Keyword(s):  
Multiobjective Optimization ◽  
Genetic Programming ◽  
Evolutionary Computation ◽  
Fast Moving ◽  
Search Space ◽  
Multiple Objectives ◽  
Experimental Results ◽  
Robotic Systems ◽  
Linear Fashion ◽  
Fast Evolution

In Genetic Programming (GP), most often the search space grows in a greater than linear fashion as the number of tasks required to be accomplished increases. This is a cause for one of the greatest problems in Evolutionary Computation (EC): scalability. The aim of the work presented here is to facilitate the evolution of control systems for complex robotic systems. The authors use a combination of mechanisms specifically designed to facilitate the fast evolution of systems with multiple objectives. These mechanisms are: a genetic transposition inspired seeding, a strongly-typed crossover, and a multiobjective optimization. The authors demonstrate that, when used together, these mechanisms not only improve the performance of GP but also the reliability of the final designs. They investigate the effect of the aforementioned mechanisms on the efficiency of GP employed for the coevolution of locomotion gaits and sensing of a simulated snake-like robot (Snakebot). Experimental results show that the mechanisms set forth contribute to significant increase in the efficiency of the evolution of fast moving and sensing Snakebots as well as the robustness of the final designs.

Genetic Algorithms and Multimodal Search

2011 ◽  
pp. 231-249
Author(s):  
Marcos Gestal ◽  
José Manuel Vázquez Naya ◽  
Norberto Ezquerra
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Computation ◽  
Unique Solution ◽  
Real World ◽  
Multiple Solutions ◽  
Global Solutions ◽  
Search Space ◽  
The Real ◽  
Local Solutions ◽  
Multimodal Problems

Traditionally, the Evolutionary Computation (EC) techniques, and more specifically the Genetic Algorithms (GAs), have proved to be efficient when solving various problems; however, as a possible lack, the GAs tend to provide a unique solution for the problem on which they are applied. Some non global solutions discarded during the search of the best one could be acceptable under certain circumstances. Most of the problems at the real world involve a search space with one or more global solutions and multiple local solutions; this means that they are multimodal problems and therefore, if it is desired to obtain multiple solutions by using GAs, it would be necessary to modify their classic functioning outline for adapting them correctly to the multimodality of such problems. The present chapter tries to establish, firstly, the characterisation of the multimodal problems will be attempted. A global view of some of the several approaches proposed for adapting the classic functioning of the GAs to the search of mu ltiple solutions will be also offered. Lastly, the contributions of the authors and a brief description of several practical cases of their performance at the real world will be also showed.

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