Paper Info
Title
GP-induced and explicit bloating of the seeds in incremental GP improves evolutionary success
Abstract
The parsimony control in genetic programming (GP) is one of the limiting factors in the quick evolution of efficient solutions. A variety of parsimony pressure methods have been developed to address this issue. The effects of these methods on the efficiency of evolution are recognized to depend on the characteristics of the applied problem domain. On the other hand, the implications of using parsimony pressure in evolving the seeds for incremental genetic programming (IGP) are still poorly known and remain uninvestigated. In this work we present a study on the cumulative effect of the bloat and the seeding of the initial population on the efficiency of incremental evolution of simulated snake-like robot (Snakebot). In the proposed IGP, the task of coevolving the locomotion gaits and sensing of the bot in a challenging environment is decomposed into two sub-tasks, implemented as two consecutive evolutionary stages. First, to evolve the pools of sensorless Snakebots, we use GP featuring the following three bloat-control methods: (1) linear parametric parsimony pressure, (2) lexicographic parsimony pressure and (3) no bloat control. During the second stage of IGP, we use these pools to seed the initial population of Snakebots applying two methods of seeding: canonical seeding and seeding inspired by genetic transposition (GT).
Year
DOI
Venue
2014
10.1007/s10710-013-9192-y
Genetic Programming and Evolvable Machines
Keywords
Field
DocType
Snakebot,Genetic programming,Bloat,Genetic transposition,Incremental GP
Population,Mathematical optimization,Problem domain,Computer science,Genetic programming,Snakebot,Parametric statistics,Artificial intelligence,Lexicographical order,Machine learning,Seeding,Limiting
Journal
Volume
Issue
ISSN
15
1
1389-2576
Citations 
PageRank 
References 
1
0.35
24
Authors
3
Name
Order
Citations
PageRank
Ivan Tanev127846.51
Tüze Kuyucu2395.64
Katsunori Shimohara3327106.53