| Description |
1 online resource |
| Series |
SpringerBriefs in intelligent systems |
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SpringerBriefs in intelligent systems
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| 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 |
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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 |
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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 |
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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 |
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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 |
| Summary |
This accessible introduction shows the reader how to understand, implement, adapt, and apply Learning Classifier Systems (LCSs) to interesting and difficult problems. The text builds an understanding from basic ideas and concepts. The authors first explore learning through environment interaction, and then walk through the components of LCS that form this rule-based evolutionary algorithm. The applicability and adaptability of these methods is highlighted by providing descriptions of common methodological alternatives for different components that are suited to different types of problems from data mining to autonomous robotics. The authors have also paired exercises and a simple educational LCS (eLCS) algorithm (implemented in Python) with this book. It is suitable for courses or self-study by advanced undergraduate and postgraduate students in subjects such as Computer Science, Engineering, Bioinformatics, and Cybernetics, and by researchers, data analysts, and machine learning practitioners |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed August 24, 2017) |
| Subject |
Learning classifier systems.
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COMPUTERS -- General.
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Learning classifier systems
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| Form |
Electronic book
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| Author |
Browne, Will N., author
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| ISBN |
9783662550076 |
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3662550075 |
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