A Simple Genetic Algorithm

For this simple project, you will be required to implement a simple genetic algorithm in C++ and run a series of experiments to test your algorithm for a given problem. Use the pseudocode given below to code your program. Fitness : One-Max Problem: fitness(individual) = Individual : Problem Representation (binary string of length L) Population : Set of individuals Mating Pool : Fixed size set of individuals selected from population to be parents One Point Crossover: Take two parents and selected random number x : 1 <= x <= L Copy genes 1-x from first parent and x+1 <= L from second Opposite for second offspring Bit-wise Mutation: Read along individual’s problem representation. In each position, flip the value according to a fixed probability Tournament Selection: Choose two individuals randomly and select the individual with higher fitness Replacement: Generational i.e. number of parents = size of mating pool = number of offspring Stopping Criterion: One of the population members has maximum fitness, or 500 generations performed Pseudocode Randomly_Initialise_Population(population) Evaluate fitness of every individual Stopping_Criterion_Met = False Generation = 0 While(Stopping_Criterion_Met == False) { Select_from_Population_into_mating_pool() Pair up mating pool at random If (rand() < crossover rate) Perform One Point Crossover to create offspring Else Copy pair of parents to offspring Endif Apply mutation operator to each offspring Evaluate each offspring Copy offspring to parents if offspring is fitter than parents Print fitness of best individual Evaluate Stopping_Criterion_Met } End. ## Deliverables 1) Complete and fully-functional working program(s) in executable form as well as complete source code of all work done. 2) Deliverables must be in ready-to-run condition, as follows (depending on the nature of the deliverables): a) For web sites or other server-side deliverables intended to only ever exist in one place in the Buyer's environment--Deliverables must be installed by the Seller in ready-to-run condition in the Buyer's environment. b) For all others including desktop software or software the buyer intends to distribute: A software installation package that will install the software in ready-to-run condition on the platform(s) specified in this bid request. 3) All deliverables will be considered "work made for hire" under U.S. Copyright law. Buyer will receive exclusive and complete copyrights to all work purchased. (No GPL, GNU, 3rd party components, etc. unless all copyright ramifications are explained AND AGREED TO by the buyer on the site per the coder's Seller Legal Agreement). ## Platform For this project, use the following parameters: Population size = 100 and Length of One-Max Problem (L) = 100. Random numbers can be generated using the rand() function found in the C standard library. To generate a number between a certain range, use the modulus operator (e.g. rand() % 100 will generate a number between 0 and 99.) Once your program has been completed and debugged, perform 3 separate sets of experiments: 1) crossover rate = 0.01, mutation rate = 0.01 2) crossover rate = 0.7, mutation rate = 0.7 3) crossover rate = 0.7, mutation rate = 0.01 windows platform create the exe file.. full commented programming all source code must be error free and run in all compiler C++ eg, bloodshed,borland C++ ,visual C++