Modeling Hydra Behavior Using Methods Founded in Behavior-Based Robotics

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54 Appendix A. Evolutionary algorithmsInitialize population❄✲ Evaluate individual❄✲ Select individuals❄Perform crossoverNo❄Mutate❄ ✏ ✏✏✏✏✏✏ ✏ New pop. complete?✏ ✏✏✏✏✏No❄Yes ✏ ✏✏✏✏✏✏ ✏ Terminate?✏ ✏✏✏✏✏✎✍❄YesEnd☞✌Figure A.1: Flow of a general EA.encoding, which was used for parametrical and structural optimization of RNNs, there isno encoding/decoding, but the EA operates directly on the structure of the individual (theRNN in this case). In real number encoding, the information is stored in chromosomes,where each gene consists of a real number within a pre-defined range.A.2 Fitness functionBased on its performance each individual is assigned a fitness value. The choice of fitnessfunction depends on the problem at hand. In this project, the fitness measure was taken asthe inverse of the RMSE over the training data set, as described in Section 7.1.2.A.3 SelectionSelection of two parents can be accomplished in several ways. In this project, tournamentselection is used. In tournament selection N individuals are randomly chosen from
Appendix A. Evolutionary algorithms 55the population, where N is referred to as the tournament size. Out of the N selectedindividuals, the best one (having the highest fitness) is selected with probability p tour .With probability 1 − p tour another, randomly chosen, individual is selected. The selectionprocedure is carried out twice, resulting in two individuals selected to generate offspring(through crossover and mutations).A.4 CrossoverThrough the use of crossover, genetic material from two parents are combined. Thecrossover operator is highly dependent on the encoding scheme, and often care must betaken in order to ensure that the resulting offspring actually encodes valid solutions tothe problem at hand. In this project, crossover was not used in the experiment involvingan RNN. In the case where an EA was used to optimize the parameters and size of ahabituation model consisting of leaky integrators, two-point crossover was used. In twopointcrossover, two crossover points are randomly chosen for each parent. The genesbetween the crossover points on one chromosome swaps places with the genes betweenthe crossover points on the other chromosome. In this way, the length of chromosomesmay be altered from parent to offspring, by the crossover operator.A.5 MutationsMutations provide the EA with new material to work with. The mutation operator isproblem specific, and several mutation operators may be used in one EA. In this projecta number of mutation operators were used. Standard mutation operators, such as fullweight mutation, where a gene simply is given a new, random value within its allowedrange, and real number creep mutation, where the change of a gene is limited in magnitude,were used to alter the parameters of the problems. In addition structural mutationoperators were implemented, to allow for optimization of the structure of the solutionas well as its parameters. Implementation details of the mutation operators used in thisproject are stated in Section 7.1.2.A.6 ElitismIn elitism a copy of the best individual is inserted in the next generation without anymodification. Hence, through the use of elitism the maximum fitness of the populationwill never decrease during the run of an EA.
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Modeling Hydra Behavior Using Metho
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Table of Contents1 Introduction 11.
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Table of ContentsviiB Recurrent neu
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2 Chapter 1. Introductione.g. in [1
Page 18 and 19: 4 Chapter 1. Introduction1.3 Outlin
Page 20 and 21: 6 Chapter 2. NotationUpper-case let
Page 22 and 23: 8 Chapter 3. Animal behaviorPsychol
Page 24 and 25: 10 Chapter 3. Animal behaviorperiod
Page 27 and 28: Chapter 4Behavior-based roboticsAs
Page 29 and 30: Chapter 5HydraThis chapter presents
Page 31 and 32: 5.1. Hydra as a model organism 17ce
Page 33 and 34: Chapter 6Models and methodsIn this
Page 35 and 36: 6.1. The simulated system 21Contrac
Page 37 and 38: 6.1. The simulated system 23Table 6
Page 39 and 40: 6.3. Constituent behaviors 25Releas
Page 41 and 42: Chapter 7Results and discussionIn t
Page 43 and 44: 7.1. Generation of behaviors 29Expe
Page 45 and 46: 7.1. Generation of behaviors 31wher
Page 47 and 48: 7.1. Generation of behaviors 33....
Page 49 and 50: 7.1. Generation of behaviors 35Tabl
Page 51 and 52: 7.1. Generation of behaviors 374504
Page 53 and 54: 7.1. Generation of behaviors 39Tran
Page 55 and 56: 7.2. Generation of behavioral organ
Page 57: 7.2. Generation of behavioral organ
Page 60 and 61: 46 Chapter 8. Summary and conclusio
Page 62 and 63: 48 Bibliography[11] BÄCK, T., FOGE
Page 64 and 65: 50 Bibliography[41] MITCHELL, M., A
Page 66 and 67: 52 Bibliography[69] WATSON, J., Beh
Page 71 and 72: Appendix BRecurrent neural networks
Page 73: Appendix B. Recurrent neural networ
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