Paper Info
Title
Incremental evolution of fast moving and sensing simulated snake-like robot with multiobjective GP and strongly-typed crossover.
Abstract
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; scalability. The aim of the work presented here is to facilitate the evolution of complex designs that have multiple features. We 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. We demonstrate that, when used together, these mechanisms not only improve the performance of GP but also the reliability of the final designs. We investigate the effect of the aforementioned mechanisms, the main focus being on genetic transposition inspired seeding and strongly typed crossover, 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 mechanism 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.
Year
DOI
Venue
2012
10.1007/s12293-012-0085-z
Memetic Computing
Keywords
Field
DocType
Incremental evolution, Seeding, Multiobjective, Genetic programming, Strongly-typed crossover, Snakebot, Locomotion
Mathematical optimization,Crossover,Evolutionary computation,Multi-objective optimization,Robustness (computer science),Genetic programming,Snakebot,Artificial intelligence,Robot,Mathematics,Machine learning,Scalability
Journal
Volume
Issue
ISSN
4
3
1865-9292
Citations 
PageRank 
References 
1
0.35
20
Authors
3
Name
Order
Citations
PageRank
Tüze Kuyucu1395.64
Ivan Tanev227846.51
Katsunori Shimohara3327106.53