Evolve Kart Unity by kleberandrade - 7

Example of application of genetic algorithm for evolution kart navigation.

Unity 2019.2.5f1MIT LicenseUpdated 1 year agoCreated on November 15th, 2019
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# Genetic Algorithm Kart

Example of application of genetic algorithm for evolution kart navigation. Project developed in the discipline of Artificial Intelligence applied to the Digital Games - Fatec Americana

## Kart Game

Racing game developed at Unity 2019.2.5

To create the track, we used 3 tiles created with ProBuilder

A small kart was also modeled in ProBuilder for simulation

## Genetic Algorithm (GA)

### Implementation

1. Generate the initial population of individuals randomly (First generation).
2. Evaluate the fitness of each individual in that population (time limit, sufficient fitness achieved, etc)
3. Repeat the following regenerational steps until termination:
4. Select the best-fit individuals for reproduction (Parents)
5. Breed new individuals through crossover and mutation operations to give birth to child.
6. Evaluate the individual fitness of new individuals.
7. Replace least-fit population with new individuals.

### Individual (Genome or Chromosome)

In the case of this kart game, I considered each gene to be a set of instructions for a kart. Each generation increases a random gene in the solution. So a typical genome could be read as:

Gene value limits

Variable Description Min value Max value
H Horizontal (steering) -1.0 1.0
V Vertical (acceleration) -1.0 1.0
D Duration (seconds) 0.05 1.0

### Fitness Function

Fitness Function (also known as the Evaluation Function) evaluates how close a given solution is to the optimum solution of the desired problem. It determines how fit a solution is.

• checkpoint is the amount of points the kart has gone through
• d is the distance between the next waypoint and the current waypoint
• D is the distance between the next waypoint and the current kart position

### Elitism

A practical variant of the general process of constructing a new population is to allow the best organism(s) from the current generation to carry over to the next, unaltered. This strategy is known as elitist selection and guarantees that the solution quality obtained by the GA will not decrease from one generation to the next.

### Tournament Selection

Tournament Selection is a Selection Strategy used for selecting the fittest candidates from the current generation in a Genetic Algorithm. These selected candidates are then passed on to the next generation. In a K-way tournament selection, we select k-individuals and run a tournament among them. Only the fittest candidate amongst those selected candidates is chosen and is passed on to the next generation. In this way many such tournaments take place and we have our final selection of candidates who move on to the next generation. It also has a parameter called the selection pressure which is a probabilistic measure of a candidate’s likelihood of participation in a tournament. If the tournament size is larger, weak candidates have a smaller chance of getting selected as it has to compete with a stronger candidate. The selection pressure parameter determines the rate of convergence of the GA. More the selection pressure more will be the Convergence rate. GAs are able to identify optimal or near-optimal solutions over a wide range of selection pressures. Tournament Selection also works for negative fitness values.

1. Select k individuals from the population and perform a tournament amongst them
2. Select the best individual from the k individuals
3. Repeat process 1 and 2 until you have the desired amount of population

### Blend Crossover and Random Mutation

In a uniform crossover, we don’t divide the chromosome into segments, rather we treat each gene separately. In this, we essentially flip a coin for each chromosome to decide whether or not it’ll be included in the off-spring. We can also bias the coin to one parent, to have more genetic material in the child from that parent.

Random mutation is an extension of the bit flip for the integer representation. In this, a random value from the set of permissible values is assigned to a randomly chosen gene.

Example of a crossover between two individuals. A mutation is visible in the child. Also, a increase of the random gene is visible in the child.

## Experiments and Results

Initial setup of the experiment

Variable Value
Population size 120 karts
Elitism 10 karts
Blend Crossover 0,20 (alpha)
Mutation rate 5%
Tournament size 3
Trial time 60 seconds

## Acknowledgment

Thanks to Professor Fernando Osorio for the ideas of the evolution strategy

## References

• ANDRADE, KLEBER O.; JOAQUIM, RICARDO C. ; CAURIN, GLAUCO A. P. ; CROCOMO, MARCIO K. . Evolutionary Algorithms for a Better Gaming Experience in Rehabilitation Robotics. COMPUTERS IN ENTERTAINMENT, v. 16, p. 1-15, 2018.
• GARCIA, B. E. R. ; ANDRADE, K. O. ; CROCOMO, M. . Dynamic Difficulty Adjustment in a Whac-A-Mole like Game. In: Simpósio Brasileiro de Games e Entretenimento Digital (SBGames), 2018, Foz do Igua¸cu ? PR. XVII Simpósio Brasileiro de Games e Entretenimento Digital, 2018.
• ANDRADE, KLEBER DE O.; PASQUAL, THALES B. ; CAURIN, GLAUCO A. P. ; CROCOMO, MARCIO K. . Dynamic difficulty adjustment with Evolutionary Algorithm in games for rehabilitation robotics. In: 2016 IEEE International Conference on Serious Games and Applications for Health (SeGAH), 2016, Orlando. 2016 IEEE International Conference on Serious Games and Applications for Health (SeGAH). p. 1-8.
• ANDRADE, K. O.; SILVA, A. E. A. ; CROCOMO, M. . Um Algoritmo Evolutivo para a Adaptação de NPCs em um Jogo de Ação. In: I Simpósio Santa Catarina Games, 2009, Florianópolis. Anais do I Simpósio Santa Catarina Games - SCGAMES, 2009.

## Licença

``````Copyright 2019 Kleber de Oliveira Andrade

Permission is hereby granted, free of charge, to any person obtaining a copy
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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SOFTWARE.
``````