Genetic Programming and Data Structures

• 1. Introduction.- 1.1 What is Genetic Programming?.- 1.2 Motivation.- 1.3 Outline.- 2. Survey.- 2.1 Introduction.- 2.2 Genetic Algorithms.- 2.3 Genetic Programming.- 2.4 GP Research.- 2.5 GP Applications.- 2.6 Conclusions.- 3. Advanced Genetic Programming Techniques.- 3.1 Background.- 3.2 Tournament Selection.- 3.3 Steady State Populations.- 3.4 Indexed memory.- 3.5 Scalar Memory.- 3.6 Multi-tree programs.- 3.7 Directed Crossover.- 3.8 Demes.- 3.9 Pareto Optimality.- 3.10 Conclusions.- 4. Evolving a Stack.- 4.1 Problem Statement.- 4.2 Architecture.- 4.3 Choice of Primitives.- 4.4 Fitness Function.- 4.5 Parameters.- 4.6 Results.- 4.7 Summary.- 5. Evolving a Queue.- 5.1 Problem Statement.- 5.2 Architecture.- 5.3 Choice of Primitives.- 5.4 Fitness Functions.- 5.5 Parameters.- 5.6 Automatically Defined Functions.- 5.7 Evolved Solutions — Caterpillar.- 5.8 Evolved Programs — Shuffler.- 5.9 Circular Buffer — Given Modulus Increment.- 5.10 Circular Buffer — Evolving Modulus Increment.- 5.11 Discussion: Building Blocks and Introns.- 5.12 Summary.- 6. Evolving a List.- 6.1 Problem Statement.- 6.2 Architecture.- 6.3 Automatically Defined Functions.- 6.4 Choice of Primitives.- 6.5 Fitness Function.- 6.6 Directed Crossover.- 6.7 Parameters.- 6.8 Results.- 6.9 Software Maintenance.- 6.10 Discussion.- 6.11 Conclusions.- 7. Problems Solved Using Data Structures.- 7.1 Balanced Bracket Problem.- 7.2 Dyck Language.- 7.3 Evaluating Reverse Polish Expressions.- 7.4 Work by Others on Solving Problems with Memory.- 7.5 Summary.- 8. Evolution of GP Populations.- 8.1 Price’s Selection and Covariance Theorem.- 8.2 Fisher’s Fundamental Theorem of Natural Selection.- 8.3 Evolution of Stack Problem Populations.- 8.4 Loss of Variety.- 8.5 Measurements of GP Crossover’s Effects.- 8.6Discussion.- 8.7 Summary.- 9. Conclusions.- 9.1 Recommendations.- 9.2 Future work.- References.- Appendices.- A–Number of Fitness Evaluations Required.- B–Glossary.- C–Scheduling Planned Maintenance of the National Grid.- C.1 Introduction.- C.2 The Electricity Transmission Network in Great Britain.- C.3 The South Wales Region of the UK Electricity Network.- C.4 Approximating Replacement Generation Costs.- C.5 The Fitness Function.- C.6 The Chromosome.- C.7 Greedy Optimisers.- C.8 South Wales Problem without Contingencies.- C.9 South Wales and Genetic Programming.- C.10 South Wales Problem with Contingencies.- C.11 Conclusions.- C.12 Future Work.- C.13 Using QGAME.- D–Implementation.- D.1 GP-QUICK.- D.2 Coding Changes to GP-QUICK-2.1.- D.3 Default Parameters.- D.4 Network Running.- D.5 Reusing Ancestors Fitness Information.- D.6 Caches.- D.7 Compressing the Check Point File.- D.8 Benchmarks.- D.9 Code.

'The book is nicely produced ... and well illustrated. The text is well written and is easy to read and understand. The concepts in genetic programming are well presented, as are those in genetic algorithms. The references are selected to be useful. People of a non-mathematical disposition will find few problems in reading this book.' Robotica, 17 (1999)