Exploring Classifier Systems: Architecture, Learning, and Evolution with Genetic Algorithms
This overview presents the fundamentals of Classifier Systems (CS), developed by Anil Shankar at the University of Nevada, Reno. It discusses the architecture of CS, detailing components like the rule and message system, credit apportionment, and the integration of genetic algorithms for rule generation. The perfect rule set is examined through a multiplexer example, highlighting the evolution and selection of rules in an information-based economy. Emphasizing parallel activation and efficiency, this presentation concludes with the advantages of employing fixed-length representation and adaptability in CS.
Exploring Classifier Systems: Architecture, Learning, and Evolution with Genetic Algorithms
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Presentation Transcript
Classifier Systems Anil Shankar Dept. of Computer Science University of Nevada, Reno
Overview • Introduction and problem overview • Architecture • Component details • Track a specific example • Summary Anil Shankar Classifier Systems
Introduction • Learning • “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E” • Machine Learning, Tom Mitchell Anil Shankar Classifier Systems
Problem Multiplexer Example Perfect Rule Set Anil Shankar Classifier Systems
Classifier System (C.S) • Learn simple string rules in an arbitrary environment • A classifier is a simple string rule • Components • Rule and Message System • Apportionment of credit system • Genetic Algorithm Anil Shankar Classifier Systems
Rule and Message System • Production system • Fixed size representation for rules • Parallel activation • Rating of a rule by an information-based economy • <message>::= { 0, 1} l • <classifier>::= <condition>:<message> • <condition>::={0, 1, #}l Anil Shankar Classifier Systems
Which classifier to choose? • Bucket Brigade Algorithm • For ranking or rating individual classifiers • Classifiers buy and sell the right to trade information (information-based economy) • Auction house and clearing house • If a classifier matches a message, it participates in an auction. • The bid (B) is proportional to its strength (S) • Once activated the winner pays its bid to other classifiers which also matched the message Anil Shankar Classifier Systems
Which classifier to choose?(contd…) • Notation • S = Strength • P = Payment • T = Tax • R = Reward • Cbid = Bid Coefficient • The ith classifier strength (at time step t) Si(t+1) = Si(t) – Pi(t) – Ti(t) + Ri(t) • Bid Bi = Cbid * Si • Tax Taxi = Ctax * Si • Effective Bid EBidi = Bi + N (σbid) • In terms of strength S(t+1) = S(t) – Cbid*S(t) – Ctax*S(t) + R(t) Anil Shankar Classifier Systems
Generating better rules • Bucket brigade algorithm evaluates rules and decides among competing alternatives. • Use a Genetic Algorithm (GA) to generate new rules • A classifier’s strength (S) is used as its fitness • Similar to the simple genetic algorithm • Entire population is not replaced at the next generation (Generation gap ) • GA period (epoch) • Number of time steps between GA calls • Time step = rule-message cycle • Crowding to maintain diversity • Mutation over a ternary alphabet {1, 0, # } Anil Shankar Classifier Systems
Generating better rules • Selection is performed using roulette-wheel selection • The GA is run according every GA Period or when conditioned on particular events (lack of match or poor performance) Anil Shankar Classifier Systems
T= 0 C.S in action (1) Strength (S) Messages (Msg) Match (M) Bid (B) CBid = 0.1 CTax = 0.0 Anil Shankar Classifier Systems
C.S in action (2) T= 1 Strength (S) Messages (Msg) Match (M) Bid (B) CBid = 0.1 CTax = 0.0 Anil Shankar Classifier Systems
C.S in action (3) T= 2 Strength (S) Messages (Msg) Match (M) Bid (B) CBid = 0.1 CTax = 0.0 Anil Shankar Classifier Systems
C.S in action (4) T= 3 Strength (S) Messages (Msg) Match (M) Bid (B) CBid = 0.1 CTax = 0.0 Anil Shankar Classifier Systems
C.S in action (5) T= 4 Strength (S) Messages (Msg) Match (M) Bid (B) CBid = 0.1 CTax = 0.0 Anil Shankar Classifier Systems
C.S in action (6) T= 5 Strength (S) CBid = 0.1 CTax = 0.0 Anil Shankar Classifier Systems
Are these rule-sets the same? Anil Shankar Classifier Systems
Multiplexer Example • Default Hierarchy • General rules cover general conditions and specific rules cover exceptions • Parsimony • Fewer rules • Enlargement of the solution set • While the problem space remains the same Anil Shankar Classifier Systems
Summary • A classifier is a simple string rule • Classifier System • rule-message system, • apportionment of credit mechanism • GA • Advantages of CS • rules are simple • use fixed length representation • parallel activation • operate in an information-based economy Anil Shankar Classifier Systems
Thank You Questions ? Anil Shankar Classifier Systems
