GA (genetic algorithm) for predator agents in pacman -- 2

Job ID: 33934777

Budget: £20 – £250 GBP

Task at hand is to improve performance of predator ghost agents using GA - The fitness is determined by testing on 100 randomly generated scenarios where the max cycle per scenario is 5000, reward strategies that are at least 3 blocks close to prey being pacman (the closer the better), the mutation rate is set at 0.01, 2 point crossover with a crossover probability of 0.4, a tournament selection scheme with a tournament size tour of 5 is used to select the parents at each generation. The following pseudocode describes the methodology: 1. Repeat the following for 10 trials on selected prey:
(a) Randomly generate a population of 100 individuals.
(b) Repeat the following until the predators show no improvement after 200
generations:
i. Simulate each predator strategy on 100 scenarios and evaluate its
fitness based on the performance on those scenarios.
ii. Select 100 individuals from the current population using Tournament
selection, pair them up, and create a new population by using 2-point
crossover with mutation.
(c) The best strategy found over all generations is used as the solution of
this trial. The fitness of this strategy is then recomputed by testing on
1000 new randomly generated scenarios.
2. The strategy that performed best over all 10 trials is used as the solution to this prey.
Related categories: Python Genetic Algorithms