Evolutionary Computation Introduction Peter Andras s

Overview 1.Biological inspiration 2.Artificial genes 3.Learning by evolution 4.Artificial evolution 5.Learning by artificial evolution

Biological inspiration Evolution: Darwin from bacteria to sponges, insects, fishes, and mammals from simple organs to complex ones from randomly spread neurons to highly organized large brains

Biological inspiration Foundations: nucleic acids: adenin, citozin, guanin, timin, uracil DNA chromosomes genes RNA, proteins, cells

Biological inspiration Adaptation by evolution: ecological niche: a set of ecological conditions (e.g., food resources, predators, other environmental risks, threats and opportunities); conquering new ecological niches (e.g., islands) development of new species that are able to use the opportunities provided by a new niche and avoid the related dangers;

Biological inspiration Adaptation: development of new behaviours and organs; new cells and cell behaviours; new proteins; new genes;

Artificial genes Idea: copying natural evolution by emulating genes and their evolution; Objective: developing adaptive solutions of some problems;

Artificial genes Artificial world: world of problems; Artificial individuals: solutions of the problems genes encode features of the problem solutions

Artificial genes Discrete feature encoding: e.g., 0 and 1 for the presence or absence of the features; chromosomes: ; the genes do not represent necessarily full features;

Artificial genes Continuous type feature encoding: e.g., features encoded by real numbers; chromosomes: multi-dimensional real vectors; usually genes directly encode features;

Learning by evolution Learning: learning = adaptation adaptation = optimisation optimisation criteria: fitness in the given environmental conditions;

Learning by evolution Exchanging and combining genes: sexual crossover + 

Learning by evolution Mutation: random changes of the genes 

Learning by evolution Inheritance: the offspring inherits the properties of their parents; some combinations are lethal; the inherited properties range from similar proteins to similar behaviours;

Learning by evolution New species: slow evolution; accumulating minor changes; modifications of organ functionality; selection of some variants of standard features (e.g., feather colours); emergence of new behaviours, organs;

Learning by evolution Mating success: features that better fit the environmental niche increase the chance of the individual to get mates and reproduce; individuals with higher fitness have more offspring; the genes of the successful individuals spread within the population and become dominant; genes that cause evolutionary advantage in mutated individuals become general;

Learning by evolution Evolutionary optimisation: increased fitness in the ecological niche; mutation is responsible for new genes (proteins, cells, organs, behaviours); crossover is responsible for passing over the new genes; fitness based mating success is responsible for the emergence of domination of genes that increase fitness;

Artificial evolution Evolution of a population of problem solutions: individuals are the problem solution; each solution is characterized by its features encoded by the genes; evolution by genetic operators and offspring generation;

Artificial evolution Mutation operator: randomly change the genes encoding the solution features; e.g., changing a 0 into a 1 and inversely; e.g., minor modification of a feature encoded by a real number;

Artificial evolution Crossover operator: defines how to select exchanged parts of the genetic material; e.g., randomly selecting a chromosome splitting position;

Artificial evolution Directed operators: preferential selection of some genes for mutation or some segments of the chromosome for crossover; the preferential selection is based on monitoring, which components of the solution contribute to bad or good performance;

Artificial evolution Constrained operators: mutation constraints: some simultaneous mutations are not allowed, others are enforced; crossover constraints: some chromosome segments are allowed to be exchanged only for some chromosome segments with specified location;

Artificial evolution Optimisation energy function: fitness measure = problem solving performance problem solving performance of the individuals are evaluated with a random sample of the potential problems;

Artificial evolution Mating potential: it is based on the problem solving performance; the number of the offspring of the individuals depends on their mating potential; high fitness individuals have many offspring that inherit at partly their features;

Artificial evolution Many parent mating: the crossover applies to the mix of all parents;

Learning by artificial evolution Problem solving performance optimisation: the average performance of the population increases; the best performing individuals represent very good solutions after long enough evolution;

Learning by artificial evolution Key features: proper feature coding; proper evolutionary operators; proper fitness evaluation; proper mating selection;

Learning by artificial evolution Feature coding: the important solution features should be encoded; if it is not clear what is important and what is not, better to encode more features than less features; the feature coding and the decoding of the code should not be ambiguous;

Learning by artificial evolution Evolutionary operators: the result of applying evolutionary operators should be meaningful; the crossover should result individuals that inherit their parents properties;

Learning by artificial evolution Fitness evaluation: the fitness function should be closely related to the effective problem solving performance;

Learning by artificial evolution Mating potential determination: the more fit individuals should have more offspring; the drastic elimination of less fit individuals may lead to the elimination of genes that are sleeping but may become important for the achievement of very high performance;

Problems: too narrow spread of performances: it is likely that there is little genetic variation in the population; too large spread of the performances: it is possible that the encoding of features or the genetic operators are not functioning properly; too slow increase of the average performance: it is possible that the encoding of features or the genetic operators are not functioning properly; Learning by artificial evolution

Summary evolution leads to niche adapted new species; the basis of evolution are the genes; new genes may lead to new proteins, cells, organs, behaviours, which may increase the fitness of the biological organism; evolutionary adaptations spread by mating and by higher mating success of those who are more fit to the ecological niche; evolutionary learning means optimisation of the fitness;

Summary artificial genes encode features of solutions of some problems, the encoding can be discrete or continuous; artificial evolution works by genetic operators; genetic operators: mutation, crossover, directed operators, constrained operators; mating potential depends on problem solving performance; having appropriate feature encoding, evolutionary operators, fitness function and mating potential determination, the artificial evolution leads to high performance solutions of the problem;