Classifier Systems

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 (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

Classifier Systems