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Table of Contents
Preface; Contents; Acronyms and Glossary; 1 LCSs in a Nutshell; Abstract; 1.1 A Non-trivial Example Problem: The Multiplexer; 1.2 Key Elements; 1.2.1 Environment; 1.2.2 Rules, Matching, and Classifiers; 1.2.3 Discovery Component
Evolutionary Computation; 1.2.4 Learning Component; 1.3 LCS Functional Cycle; 1.4 Post-training; 1.4.1 Rule Compaction; 1.4.2 Prediction; 1.4.3 Evaluation; 1.4.3.1 Training & Testing Performance; 1.4.3.2 Significance of Performance; 1.4.4 Interpretation; 1.5 Code Exercises (eLCS); 2 LCS Concepts; Abstract; 2.1 Learning; 2.1.1 Modeling with a Ruleset; 2.2 Classifier
2.2.1 Rules2.2.1.1 Rule Worth; 2.2.1.2 Rules Versus Classifiers; 2.2.1.3 Niche; 2.2.2 Representation and Alphabet; 2.2.3 Generalisation; 2.2.3.1 Don't Care '#' Operator; 2.2.3.2 Overgeneral Rules; 2.2.3.3 Overspecific Rules; 2.2.3.4 Maximally General, Accurate Rules; 2.3 System; 2.3.1 Interaction with Problems; 2.3.1.1 Environment Properties; 2.3.1.2 Learning, Adaptive, and Cognitive Systems; 2.3.1.3 Evaluating Rules; 2.3.2 Cooperation of Classifiers; 2.3.3 Competition Between Classifiers; 2.4 Problem Properties; 2.4.1 Problem Complexity; 2.4.1.1 Size of Search Space
2.4.1.2 Redundancy and Irrelevance2.4.1.3 Epistasis; 2.4.1.4 Heterogeneity; 2.4.2 Applications Overview; 2.5 Advantages; 2.6 Disadvantages; 3 Functional Cycle Components; Abstract; 3.1 Evolutionary Computation and LCSs; 3.2 Initial Considerations; 3.3 Basic Alphabets for Rule Representation; 3.3.1 Encoding for Binary Alphabets; 3.3.2 Interval-Based; 3.3.2.1 Hyperalphabets; 3.3.2.2 Mixed Representations; 3.4 Matching; 3.5 Covering; 3.6 Form a Correct Set or Select an Action; 3.6.1 Explore vs. Exploit; 3.6.1.1 Local Optima; 3.6.2 Action Selection; 3.7 Performing the Action; 3.8 Update
3.8.1 Numerosity of Rules3.8.2 Fitness Sharing; 3.9 Selection for Rule Discovery; 3.9.1 Parent Selection Methods; 3.9.1.1 Roulette Wheel Selection; 3.9.1.2 Tournament Selection; 3.10 Rule Discovery; 3.10.1 When to Invoke Rule Discovery; 3.10.2 Identifying Building Blocks of Knowledge; 3.10.3 Mutation; 3.10.4 Crossover; 3.10.4.1 Single-Point, Two-Point, or Uniform Crossover; 3.10.5 Initialising Offspring Classifiers; 3.10.6 Other Rule Discovery; 3.11 Subsumption; 3.12 Deletion; 3.13 Summary; 4 LCS Adaptability; Abstract; 4.1 LCS Pressures; 4.2 Michigan-Style vs. Pittsburgh-Style LCSs
4.3 Michigan-Style Approaches4.3.1 Michigan-Style Supervised Learning (UCS); 4.3.2 Updates with Time-Weighted Recency Averages; 4.3.3 Michigan-Style Reinforcement Learning (e.g. XCS); 4.3.3.1 XCS; 4.3.3.2 Zeroth-Level Classifier System (ZCS); 4.3.3.3 Older Michigan-Style LCSs; 4.3.3.4 ExSTraCS; 4.4 Pittsburgh-Style Approaches; 4.4.1 GAssist and BioHEL; 4.4.2 GABIL, GALE, and A-PLUS; 4.5 Strength- vs. Accuracy-Based Fitness; 4.5.1 Strength-Based; 4.5.2 Accuracy-Based; 4.6 Niche-Based Rule Discovery; 4.7 Single- vs. Multi-step Learning; 4.7.1 Sense, Plan, Act; 4.7.2 Delayed Reward
Evolutionary Computation; 1.2.4 Learning Component; 1.3 LCS Functional Cycle; 1.4 Post-training; 1.4.1 Rule Compaction; 1.4.2 Prediction; 1.4.3 Evaluation; 1.4.3.1 Training & Testing Performance; 1.4.3.2 Significance of Performance; 1.4.4 Interpretation; 1.5 Code Exercises (eLCS); 2 LCS Concepts; Abstract; 2.1 Learning; 2.1.1 Modeling with a Ruleset; 2.2 Classifier
2.2.1 Rules2.2.1.1 Rule Worth; 2.2.1.2 Rules Versus Classifiers; 2.2.1.3 Niche; 2.2.2 Representation and Alphabet; 2.2.3 Generalisation; 2.2.3.1 Don't Care '#' Operator; 2.2.3.2 Overgeneral Rules; 2.2.3.3 Overspecific Rules; 2.2.3.4 Maximally General, Accurate Rules; 2.3 System; 2.3.1 Interaction with Problems; 2.3.1.1 Environment Properties; 2.3.1.2 Learning, Adaptive, and Cognitive Systems; 2.3.1.3 Evaluating Rules; 2.3.2 Cooperation of Classifiers; 2.3.3 Competition Between Classifiers; 2.4 Problem Properties; 2.4.1 Problem Complexity; 2.4.1.1 Size of Search Space
2.4.1.2 Redundancy and Irrelevance2.4.1.3 Epistasis; 2.4.1.4 Heterogeneity; 2.4.2 Applications Overview; 2.5 Advantages; 2.6 Disadvantages; 3 Functional Cycle Components; Abstract; 3.1 Evolutionary Computation and LCSs; 3.2 Initial Considerations; 3.3 Basic Alphabets for Rule Representation; 3.3.1 Encoding for Binary Alphabets; 3.3.2 Interval-Based; 3.3.2.1 Hyperalphabets; 3.3.2.2 Mixed Representations; 3.4 Matching; 3.5 Covering; 3.6 Form a Correct Set or Select an Action; 3.6.1 Explore vs. Exploit; 3.6.1.1 Local Optima; 3.6.2 Action Selection; 3.7 Performing the Action; 3.8 Update
3.8.1 Numerosity of Rules3.8.2 Fitness Sharing; 3.9 Selection for Rule Discovery; 3.9.1 Parent Selection Methods; 3.9.1.1 Roulette Wheel Selection; 3.9.1.2 Tournament Selection; 3.10 Rule Discovery; 3.10.1 When to Invoke Rule Discovery; 3.10.2 Identifying Building Blocks of Knowledge; 3.10.3 Mutation; 3.10.4 Crossover; 3.10.4.1 Single-Point, Two-Point, or Uniform Crossover; 3.10.5 Initialising Offspring Classifiers; 3.10.6 Other Rule Discovery; 3.11 Subsumption; 3.12 Deletion; 3.13 Summary; 4 LCS Adaptability; Abstract; 4.1 LCS Pressures; 4.2 Michigan-Style vs. Pittsburgh-Style LCSs
4.3 Michigan-Style Approaches4.3.1 Michigan-Style Supervised Learning (UCS); 4.3.2 Updates with Time-Weighted Recency Averages; 4.3.3 Michigan-Style Reinforcement Learning (e.g. XCS); 4.3.3.1 XCS; 4.3.3.2 Zeroth-Level Classifier System (ZCS); 4.3.3.3 Older Michigan-Style LCSs; 4.3.3.4 ExSTraCS; 4.4 Pittsburgh-Style Approaches; 4.4.1 GAssist and BioHEL; 4.4.2 GABIL, GALE, and A-PLUS; 4.5 Strength- vs. Accuracy-Based Fitness; 4.5.1 Strength-Based; 4.5.2 Accuracy-Based; 4.6 Niche-Based Rule Discovery; 4.7 Single- vs. Multi-step Learning; 4.7.1 Sense, Plan, Act; 4.7.2 Delayed Reward