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 Portfolio theory
1. Diversification (value)
2. Efficient frontier
    2.1 Geometrical interpretation of critical lines
           method (3 assets)
    2.2 Geometrical interpretation of critical lines
           method (4 assets)
    2.3 Critical lines method
3. Diversification (quantity)
Part III Genetic algorithm model
1. Nonstandard genetic algorithm
     1.1 Real coded optimization
            1.1.1 Chromosome
            1.1.2 Fitness function
            1.1.3 Genetic operators
            1.1.4 Stopping criteria
            1.1.5 Schematic diagram
    1.2 Discrete optimization
Part IV Conclusions
 1. Test results
 2. Improvement trends
References