Modeling Hydra Behavior Using Methods Founded in Behavior-Based Robotics

Appendix AEvolutionary algorithmsIn this appendix, a brief introduction to EAs is given. Numerous literature is available onthe subject, for a more thorough review see e.g. [11, 41, 67].EAs is an umbrella term for search algorithms inspired by evolution in nature. Differingsomewhat in representation and implementation details, versions of EAs includegenetic algorithms (GAs), evolution strategies (ES), and genetic programming (GP) [67].Suited for problems with large search space and many local optima, EAs have been usedin a wide variety of application areas, such as function optimization, scheduling, constructionof ANNs, system identification, and time series prediction. An EA operates ona population, which consists of several individuals. An individual can be seen as a candidatesolution for the task at hand, and its information is contained in one or more chromosomes,forming the genome of the individual. Each chromosome, in turn, consists ofa set of genes, in which the information is encoded. During the flow of an EA, whichcan be seen if Fig. A.1, each individual is evaluated and assigned a fitness, i.e. a measureof goodness, based of its ability to solve the given task. The EA operators selection,crossover, and mutation are then applied, forming the individuals of the next generation.During selection, two individuals are chosen for reproduction, and fitter individualsstand a better chance of being selected than less fit ones. The crossover procedure combinesinformation from the selected individuals, and mutations randomly change piecesof information encoded in the genome of an individual, inserting new genetic material inthe population.A.1 Encoding schemeThere are several ways of encoding the information contained in the genome of an individual.In this project, explicit encoding, and real-number encoding is used. In explicit53