Linear Genetic Programming

About the authors -- 1. Introduction -- 1.1 Evolutionary algorithms -- 1.2 Genetic programming -- 1.3 Linear genetic programming -- 1.4 Motivation -- Part I. Fundamental analysis -- 2. Basic concepts of linear genetic programmning -- 2.1 Representation of programs -- 2.2 Execution of programs -- 2.3 Evolution of programs -- 3. Characteristics of the linear representation -- 3.1 Effective code and noneffective code -- 3.2 Structural introns and semantic introns -- 3.3 Graph interpretation -- 3.4 Analysis of program structure -- 3.5 Graph evolution -- 3.6 Summary and conclusion -- 4. A comparison with neural networks -- 4.1 Medical data mining -- 4.2 Benchmark data sets -- 4.3 Experimental setup -- 4.4 Experiments and comparison -- 4.5 Summary and conclusion -- Part II. Method design -- 5. Segment variations -- 5.1 Variation effects -- 5.2 Effective variation and evaluation -- 5.3 Variation step size -- 5.4 Causality -- 5.5 Selection of variation points -- 5.6 Characteristics of variation operators -- 5.7 Segment variation operators -- 5.8 Experimental setup -- 5.9 Experiments -- 5.10 Summary and conclusion -- 6. Instruction mutations -- 6.1 Minimum mutation step size -- 6.2 Instruction mutation operators -- 6.3 Experimental setup -- 6.4 Experiments -- 6.5 Summary and conclusion -- 7. Analysis of control parameters -- 7.1 Number of registers -- 7.2 Number of output registers -- 7.3 Rate of constants -- 7.4 Population size -- 7.5 Maximum program length -- 7.6 Initialization of linear programs -- 7.7 Constant program length -- 7.8 Summary and conclusion -- 8. A comparison with tree-based GP -- 8.1 Tree-based genetic programming -- 8.2 Benchmark problems -- 8.3 Experimental setup -- 8.4 Experiments and comparison -- 8.5 Discussion -- 8.6 Summary and conclusion -- Part III. Advanced techniques and phenomena -- 9. Control of diversity and variation step size -- 9.1 Introduction -- 9.2 Structural program distance -- 9.3 Semantic program distance -- 9.4 Control of diversity -- 9.5 Control of variation step size -- 9.6 Experimental setup -- 9.7 Experiments -- 9.8 Alternative selection criteria -- 9.9 Summary and conclusion -- 10. Code growth and neutral variations --10.1 Code growth in GP -- 10.2 Proposed causes of code growth -- 10.3 Influence of variation step size -- 10.4 Neutral variations -- 10.5 Conditional reproduction and variation -- 10.6 Experimental setup -- 10.7 Experiments -- 10.8 Control of code growth -- 10.9 Summary and conclusion -- 11. Evolution of program teams -- 11.1 Introduction -- 11.2 Team evolution -- 11.3 Combination of multiple predictors -- 11.4 Experimental setup -- 11.5 Experiments -- 11.6 Combination of multiple program outputs -- 11.7 Summary and conclusion -- Epilogue.