Paper Info
Title
Evolutionary Multi-Objective Optimization Of Robustness And Innovation In Redundant Genetic Representations
Abstract
Robustness and innovation are two essential facets for biological evolution, where robustness means the relative insensitivity of an organism's phenotype to mutations, while innovation (evolvability) denotes the individual's ability to evolve novel phenotypes that help its survival and reproduction. Although much research has been conducted on robustness and evolvability of both biological and computational evolutionary systems, little work on the quantitative analysis of the relationship between robustness and evolvability has been reported. In this work, a measure for innovation called local variability has been suggested. Based on a neutrality degree borrowed from literature [1] and local variability, a multi-objective evolutionary algorithm has been employed to maximize the robustness and innovation by optimizing the genotype-phenotype mapping of the redundant representation. The obtained Pareto-optimal solutions are then analyzed to reveal the trade-off relationship between robustness and innovation of the redundant representation.
Year
DOI
Venue
2009
10.1109/MCDM.2009.4938826
MCDM: 2009 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN MULTI-CRITERIA DECISION-MAKING
Keywords
Field
DocType
genetic engineering,boolean algebra,encoding,evolutionary computation,biology,robustness,quantitative analysis,pareto analysis,optimization,genetics,boolean functions
Boolean function,Mathematical optimization,Evolutionary algorithm,Evolvability,Computer science,Evolutionary computation,Robustness (computer science),Multi-objective optimization,Artificial intelligence,Pareto analysis,Machine learning,Encoding (memory)
Conference
Citations 
PageRank 
References 
8
0.67
6
Authors
4
Name
Order
Citations
PageRank
Yaochu Jin16457330.45
Robin Gruna2144.50
Ingo Paenke3332.61
Bernhard Sendhoff42272240.31