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Computer Science - Artificial Intelligence | Grammatical Evolution - Evolutionary Automatic Programming in an Arbitrary Language

Grammatical Evolution

Evolutionary Automatic Programming in an Arbitrary Language

Series: Genetic Programming, Vol. 4

O'Neill, Michael, Ryan, Conor

2003, XVI, 144 p.

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Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language provides the first comprehensive introduction to Grammatical Evolution, a novel approach to Genetic Programming that adopts principles from molecular biology in a simple and useful manner, coupled with the use of grammars to specify legal structures in a search. Grammatical Evolution's rich modularity gives a unique flexibility, making it possible to use alternative search strategies - whether evolutionary, deterministic or some other approach - and to even radically change its behavior by merely changing the grammar supplied. This approach to Genetic Programming represents a powerful new weapon in the Machine Learning toolkit that can be applied to a diverse set of problem domains.

Content Level » Research

Keywords » Extension - algorithms - behavior - code - evolution - genetic algorithms - genetic programming - grammar - grammars - learning - logic - logic programming - machine learning - programming - search strategy

Related subjects » Artificial Intelligence - Computer Science - Theoretical Computer Science

Table of contents 

1. Introduction.- 1 Evolutionary Automatic Programming.- 2 Molecular Biology.- 3 Grammars.- 4 Outline.- 2. Survey of Evolutionary Automatic Programming.- 1 Introduction.- 2 Evolutionary Automatic Programming.- 3 Origin of the Species.- 4 Tree-based Systems.- 4.1 Genetic Programming.- 4.2 Grammar based Genetic Programming.- 4.2.1 Backus Naur Form.- 4.2.2 Cellular Encoding.- 4.2.3 Bias in GP.- 4.2.4 Genetic Programming Kernel.- 4.2.5 Combining GP and ILP.- 4.2.6 Auto-parallelisation with GP.- 5 String based GP.- 5.1 BGP.- 5.2 Machine Code Genetic Programming.- 5.3 Genetic Algorithm for Deriving Software.- 5.4 CFG/GP.- 6 Conclusions.- 3. Lessons from Molecular Biology.- 1 Introduction.- 2 Genetic Codes & Gene Expression Models.- 3 Neutral Theory of Evolution.- 4 Further Principles.- 5 Desirable Features.- 6 Conclusions.- 4. Grammatical Evolution.- 1 Introduction.- 2 Background.- 3 Grammatical Evolution.- 3.1 The Biological Approach.- 3.2 The Mapping Process.- 3.2.1 Backus Naur Form.- 3.2.2 Mapping Process Outline.- 3.3 Example Individual.- 3.4 Genetic Code Degeneracy.- 3.5 The Search Algorithm.- 4 Discussion.- 5 Conclusions.- 5. Four Examples of Grammatical Evolution.- 1 Introduction.- 2 Symbolic Regression.- 2.1 Results.- 3 Symbolic Integration.- 3.1 Results.- 4 Santa Fe Ant Trail.- 4.1 Results.- 5 Caching Algorithms.- 5.1 Results.- 6 Conclusions.- 6. Analysis of Grammatical Evolution.- 1 Introduction.- 2 Wrapping Operator.- 2.1 Results.- 2.1.1 Invalid Individuals.- 2.1.2 Cumulative Frequency of Success.- 2.1.3 Genome Lengths.- 2.2 Discussion.- 3 Degenerate Genetic Code.- 3.1 Results.- 3.1.1 Diversity Measures.- 3.2 Discussion.- 4 Removal of Wrapping and Degeneracy.- 4.1 Results.- 5 Mutation Rates.- 5.1 Results.- 6 Conclusions.- 7. Crossover in Grammatical Evolution.- 1 Introduction.- 2 Homologous Crossover.- 2.1 Experimental Approach.- 2.2 Results.- 2.3 Discussion.- 3 Headless Chicken.- 3.1 Experimental Approach.- 3.2 Results.- 3.3 Discussion.- 4 Conclusions.- 8. Extensions & Applications.- 1 Translation.- 2 Alternative Search Strategies.- 3 Grammar Defined Introns.- 4 GAUGE.- 4.1 Problems.- 4.1.1 Onemax.- 4.1.2 Results.- 4.2 Mastermind - a deceptive ordering version.- 4.2.1 Results.- 4.3 Discussion.- 4.4 Conclusions and Future Work.- 5 Chorus.- 5.1 Example Individual.- 5.2 Results.- 6 Financial Prediction.- 6.1 Trading Market Indices.- 6.1.1 Experimental Setup & Results.- 7 Adaptive Logic Programming.- 7.1 Logic Programming.- 7.2 GE and Logic Programming.- 7.2.1 Backtracking.- 7.2.2 Initialisation.- 7.3 Discussion.- 8 Sensible Initialisation.- 9 Genetic Programming.- 10 Conclusions.- 9. Conclusions & Future Work.- 1 Summary.- 2 Future Work.

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