Figure 1.1

The Evolutionary Equation.

Figure 1.2

The Soft Computing Equation.

Figure 1.3

Skeleton of an Evolutionary Algorithm.

Figure 1.4

A GA Proceeds in an Iterative Manner.

Figure 1.5

Working Sheet of a GA.

Figure 1.6

A possible Classification of Search Techniques.

Figure 2.1

GA at Work.

Figure 2.2

The Concept of Schema.

Figure 2.3

Derivation of The Schema Theorem.

Figure 2.4

The Schema Theorem.

Figure 2.5

Some Examples of New Crossover Techniques: Single-Point, Double-Point, Uniform and Arithmetic Crossover Operators.

Figure 2.6

A Steady-State GA Architecture with Different Operators.

Figure 2.7

Symbols Tree.

Figure 3.1

Three Level GANN Design.

Figure 4.1

One Possible Classification of Parallel GAs.

Figure 4.2

A Farmed GA.

Figure 4.3

A Distributed (Migration) GA.

Figure 4.4

A Massively Parallel (Cellular) GA.

Figure 4.6

Proposal of working environment for a PGA software system.

Figure 4.7

Traditional imperative pseudocode of a GA.

Figure 4.8

The shape and semantics of a GA.

Figure 4.9

Proposal of a hierarchy of dGA classes (dGANN).

Figure 4.10

The dGAME model.

Figure 6.1

Steps of a GP.

Figure 6.2

The Cart Centering Problem.

Figure 6.3

Phases of fuzzy interpreter.

Figure 6.4

Fuzzy sets for position, velocity and force.

Figure 6.5

A syntactic tree and the rule it represents.

Figure 7.1

Tabu Search method.

Figure 8.1

Direct collaboration scenario.

Figure 8.2

Outline of the Hybrid Genetic Algorithm (HGA).

Figure 8.3

Outline of the Asynchronous Hybrid Genetic Algorithm (AHGA).

Figure 8.4

Outline of the parallel evolution model.

This page was last updated on 20-Apr-98