A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming

Authors:
Marco Tomassini;Leonardo Vanneschi;Philippe Collard;Manuel Clergue
Affiliations:
Information Systems Department, Lausanne University, 1015 Lausanne, Switzerland;Information Systems Department, Lausanne University, 1015 Lausanne, Switzerland;Laboratoire I3S, Nice Sophia Antipolis University, France;Laboratoire I3S, Nice Sophia Antipolis University, France
Venue:
Evolutionary Computation
Year:
2005

Citing 20
Cited 43

Generalizing the notion of schema in genetic algorithms

Artificial Intelligence
Genetic programming (videotape): the movie

Genetic programming (videotape): the movie
What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation

Machine Learning - Special issue on genetic algorithms
The royal tree problem, a benchmark for single and multiple population genetic programming

Advances in genetic programming
Foundations of genetic programming

Foundations of genetic programming
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
What Makes a Problem GP-Hard? Analysis of a Tunably Difficult Problem in Genetic Programming

Genetic Programming and Evolvable Machines
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms

Proceedings of the 6th International Conference on Genetic Algorithms
Fitness Distance Correlation and Ridge Functions

PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Long Path Problems

PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Fitness Distance Correlation And Problem Difficulty For Genetic Programming

GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Maintaining the Diversity of Genetic Programs

EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Concepts of Inductive Genetic Programming

EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Genetic Programming IV: Routine Human-Competitive Machine Intelligence

Genetic Programming IV: Routine Human-Competitive Machine Intelligence
An adverse interaction between crossover and restricted tree depth in genetic programming

GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
What makes a problem GP-hard? validating a hypothesis of structural causes

GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Convergence of program fitness landscapes

GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Difficulty of unimodal and multimodal landscapes in genetic programming

GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Fitness distance correlation in structural mutation genetic programming

EuroGP'03 Proceedings of the 6th European conference on Genetic programming
A comparison of predictive measures of problem difficulty inevolutionary algorithms

IEEE Transactions on Evolutionary Computation

Scalable estimation-of-distribution program evolution

Proceedings of the 9th annual conference on Genetic and evolutionary computation
Fitness-proportional negative slope coefficient as a hardness measure for genetic algorithms

Proceedings of the 9th annual conference on Genetic and evolutionary computation
On the behavioral diversity of random programs

Proceedings of the 9th annual conference on Genetic and evolutionary computation
Towards an optimal restart strategy for genetic programming

Proceedings of the 9th annual conference on Genetic and evolutionary computation
Using quotient graphs to model neutrality in evolutionary search

Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Dealings with problem hardness in genetic algorithms

WSEAS Transactions on Computers
The new negative slope coefficient measure

EC'09 Proceedings of the 10th WSEAS international conference on evolutionary computing
Program Evolution for General Intelligence

Proceedings of the 2007 conference on Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the AGI Workshop 2006
Using crossover based similarity measure to improve genetic programming generalization ability

Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Fitness landscapes and problem hardness in genetic programming

Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Analysis of constant creation techniques on the binomial-3 problem with grammatical evolution

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
An Empirical Investigation of How Degree Neutrality Affects GP Search

MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
On the effects of locality in a permutation problem: the Sudoku puzzle

CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
On the effects of bit-wise neutrality on fitness distance correlation, phenotypic mutation rates and problem hardness

FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
The effects of constant neutrality on performance and problem hardness in GP

EuroGP'08 Proceedings of the 11th European conference on Genetic programming
An overview of AI research in Italy

Artificial intelligence
Towards an understanding of locality in genetic programming

Proceedings of the 12th annual conference on Genetic and evolutionary computation
Fitness landscapes and problem hardness in genetic programming

Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Theoretical results in genetic programming: the next ten years?

Genetic Programming and Evolvable Machines
Open issues in genetic programming

Genetic Programming and Evolvable Machines
Practical performance models of algorithms in evolutionary program induction and other domains

Artificial Intelligence
A fine-grained view of GP locality with binary decision diagrams as ant phenotypes

PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Predicting problem difficulty for genetic programming applied to data classification

Proceedings of the 13th annual conference on Genetic and evolutionary computation
The K landscapes: a tunably difficult benchmark for genetic programming

Proceedings of the 13th annual conference on Genetic and evolutionary computation
Performance models for evolutionary program induction algorithms based on problem difficulty indicators

EuroGP'11 Proceedings of the 14th European conference on Genetic programming
How far is it from here to there? a distance that is coherent with GP operators

EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Defining locality as a problem difficulty measure in genetic programming

Genetic Programming and Evolvable Machines
Some steps towards understanding how neutrality affects evolutionary search

PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A survey of problem difficulty in genetic programming

AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
Negative slope coefficient: a measure to characterize genetic programming fitness landscapes

EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Using subtree crossover distance to investigate genetic programming dynamics

EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Addressing stability issues in mediated complex contract negotiations for constraint-based, non-monotonic utility spaces

Autonomous Agents and Multi-Agent Systems
A distance between populations for one-point crossover in genetic algorithms

Theoretical Computer Science
Fitness distance correlation and search space analysis for permutation based problems

EvoCOP'10 Proceedings of the 10th European conference on Evolutionary Computation in Combinatorial Optimization
Exact computation of the fitness-distance correlation for pseudoboolean functions with one global optimum

EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
Genetic programming needs better benchmarks

Proceedings of the 14th annual conference on Genetic and evolutionary computation
A comparative study of an evolvability indicator and a predictor of expected performance for genetic programming

Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Better GP benchmarks: community survey results and proposals

Genetic Programming and Evolvable Machines
Introducing graphical models to analyze genetic programming dynamics

Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
Problem understanding through landscape theory

Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Models of performance of evolutionary program induction algorithms based on indicators of problem difficulty

Evolutionary Computation
A model for analysing the collective dynamic behaviour and characterising the exploitation of population-based algorithms

Evolutionary Computation
From problems to protocols: Towards a negotiation handbook

Decision Support Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present an approach to genetic programming difficulty based on a statistical study of program fitness landscapes. The fitness distance correlation is used as an indicator of problem hardness and we empirically show that such a statistic is adequate in nearly all cases studied here. However, fitness distance correlation has some known problems and these are investigated by constructing an artificial landscape for which the correlation gives contradictory indications. Although our results confirm the usefulness of fitness distance correlation, we point out its shortcomings and give some hints for improvement in assessing problem hardness in genetic programming.