NDLI: Using multivariate quantitative genetics theory to assist in EA customization

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Analysis of evolutionary algorithms: from computational complexity analysis to algorithm engineering

On the behaviour of the (1,λ)-es for a simple constrained problem

Elementary bit string mutation landscapes

On the practicality of optimal output mechanisms for co-optimization algorithms

Handling expensive optimization with large noise

Computational complexity analysis of simple genetic programming on two problems modeling isolated program semantics

The logarithmic hypervolume indicator

Approximating the distribution of fitness over hamming regions

The role of selective pressure when solving symmetric functions in polynomial time

Runtime analysis of the (1+1) evolutionary algorithm on strings over finite alphabets

Analyzing the impact of mirrored sampling and sequential selection in elitist evolution strategies

Using markov-chain mixing time estimates for the analysis of ant colony optimization

Abstract convex evolutionary search

Faster black-box algorithms through higher arity operators

Non-uniform mutation rates for problems with unknown solution lengths

Adaptive population models for offspring populations and parallel evolutionary algorithms

On the movement of vertex fixed points in the simple GA

Simple max-min ant systems and the optimization of linear pseudo-boolean functions

Using multivariate quantitative genetics theory to assist in EA customization

Towards the geometry of estimation of distribution algorithms based on the exponential family

Convergence rates of SMS-EMOA on continuous bi-objective problem classes

Using multivariate quantitative genetics theory to assist in EA customization

Content Provider	ACM Digital Library
Author	Bassett, Jeffrey Kermes De Jong, Kenneth Alan
Abstract	Customizing and evolutionary algorithm (EA) for a new or unusual problem can seem relatively simple as long as one can devise an appropriate representation and reproductive operators to modify it. Unfortunately getting a customized EA to produce high quality results in a reasonable amount of time can be quite challenging. There is little guidance available to help practitioners deal with this issue. Most evolutionary computation (EC) theory is only applicable to specific representations, or assumes knowledge of the fitness function, such as the location of optima. We are developing an approach based on theory from the biology community to address this problem. Multivariate quantitative genetics theory characterizes evolving populations as multivariate probability distributions of phenotypic traits. Some advantages it offers are a degree of independence from the underlying representation, and useful concepts such as phenotypic heritability. Re-working the quantitative genetics equations, we expose an additional term that we call "perturbation". We believe that perturbation and heritability provide quantitative measures of the exploration and exploitation, and that practitioners can use these to identify and diagnose imbalances in customized reproductive operators. To illustrate, we use these tools to diagnose problems with a standard recombination operator for a Pittsburgh approach classifier system. With this knowledge we develop a new, more balanced, recombination operator, and show that its use leads to significantly better results.
Starting Page	219
Ending Page	230
Page Count	12
File Format	PDF
ISBN	9781450306331
DOI	10.1145/1967654.1967674
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-01-05
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Quantitative genetics Customization Evolutionary computation Heritability
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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