Roultte Wheel Selection in Genetic Algorithm
Roulette Wheel was one of most common method among selection methods in GA. It start after generate population of chromosome work has been completed. The problem is how to select individual population in crossovering method. Before selecting each, every individual population need to calculate their fitness and rank it by good solution in higher and lower for less good solution. Then it can represent by a big round cake or circle. The big partition cake for good solution and small partition cake for less good solution. The idea was every good solution will have more probability compare with less good solution. This picture below are taken from others website. Population no. 2 have small shape because of less good solution compare with no. 2 and no.3 where it was more good solution chromosome.
After that throw the dice to get random number, then two selection from population cromosome for crossover process. It also can be use to select which population that can be use for mutation process. Finally we can select the best solution among every population, crossover and mutation process. These idea for fast searching solution compare with conventional method. Thank you for reading.
AlhamduliLah today I was attending ICSECS09 conference that held at Swiss Garden & Spa, Kuantan, Pahang and it was my 4'th conferences that I have attended. I quite happy despite Alex, Ira & also master sifu Sir Suhaimi beside others researcher from other places in others background to participate in here. Everything work so fine and with Mr Suhaimi always entertain joke & tips that make me stay calm before my presentation. Very thank you to UMP which is the organizer for great hospitality. 
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