Contents

Introduction
Part I Genetic algorithms theory
1. Simple genetic algorithm
    1.1 Simple description
    1.2 Mathematical definition
2. Mathematical considerations
3. Improvements on simple genetic algorithm
    3.1 Genotype representation, diploid and
            domination
    3.2 Selection methods
           3.2.1 Improvements on standard selection
                       method
           3.2.2 Niches and species – optimization of
                      multimodal functions
    3.3 Genetic operators
           3.3.1 Crossover
           3.3.2 Order changing operators- inversion,
                      PMX, OX, CX, mutation
    3.4 Fitness function characteristics
           3.4.1 Fitness transformation
           3.4.2 Fitness scaling
           3.4.3 Constraints
    3.5 Multi-criteria optimization
    3.6 Improvement trends
    3.7 Real coded solutions
Part II Financial applications
1. Nonstandard genetic algorithm for stock portfolio
     diversification
    1.1 Problem definition
    1.2 Genetic algorithm model
           1.2.1 Real coded optimization
                      1.2.1.1 Chromosome
                      1.2.1.2 Fitness function
                      1.2.1.3 Genetic operators
                      1.2.1.4 Stopping criteria
           1.2.2 Discrete optimization
    1.3 Tests on portfolio problem
           1.3.1 Test results
           1.3.2 Conclusions
2. Stock index tracking – passive portfolio
     management
    2.1 Quadratic programming
    2.2 Shapcott’s genetic algorithm model
    2.3 Test results
3. Bankruptcy forecasting
    3.1 Test problems
    3.2 Genetic algorithm model
    3.3 Test results
4. Protection against bank computer system’s
     hackers
    4.1 Method
    4.2 Drakos’ genetic algorithm model
    4.3 Test results
5. Operation models on financial markets
    5.1 Elements of simple model
    5.2 Indexes
    5.3 Operations on indexes
    5.4 Model effectiveness
    5.5 Genetic algorithm model
    5.6 Test results
6. Solvency appraisal
    6.1 Problem domain
    6.2 OMEGA
    6.3 Genetic programming
           6.3.1 First step - definitions
           6.3.2 Second step – application of genetic
                      algorithm
    6.4 GAFF
    6.5 Test results
7. Stock exchange forecasting
    7.1 Neural network
    7.2 Genetically evolving neural networks
    7.3 Example of application
References