A novel genetic algorithm and its application to optimize manufacturing schedules
John Holland and his colleagues at the University of Michigan introduced genetic algorithms (GAs) in 1992. The algorithmic coding of Genetic Algorithms was described by Goldberg. Since of the real world problems studied in operations research and management science are too complex to be solved by using conventional optimization techniques, genetic algorithms have been widely used in their solution. Furthermore, problems with stochastic characteristics are also typical in analysis, design, and operation of modern systems. Stochastic optimization methods are even more complex than deterministic methods. Since the 1960’s researches have tried to simulate biological process for solving hard optimization problems including stochastic optimization problems. Evolutionary Algorithms (EAs) have been introduced to imitate natural evolutionary processes of human beings. The Genetic Algorithm is an example of EAs. Simulated Annealing and Genetic Algorithms are two examples of optimization methods applied to solving stochastic optimization problems. ^ According to Darwin Evolution’s theory, a process must exist which determines how traits get passed from one generation to the next. Moreover, there must always be diversity of traits present in the population. The last element of Darwin’s Theory is natural selection, which is a way to protect the functional advantages that enables a species have an advantage in competition with others in nature. John Holland used these ideas from Darwin’s Theory when he introduced the Genetic Algorithm. GAs initiate with a set of random solutions for the problem, and this set of solutions is called the population. Each individual (random solution) in a population is called a chromosome and satisfies the constraints for the problem. To facilitate convergence and make the algorithm less sensitive to modeling error, randomness is occasionally used in the search process. This dissertation, which proposes, tests and utilizes a new approach for GAs, will be discussed in four manuscripts: ^ The objective of Manuscript I (in preparation for submission to the journal of Association for Computation Machinery) was to modify the conventional concept of Genetic Algorithm base on human cell division mechanisms. Based on an undirected mechanism of evolution and the natural selection processes, genetic algorithms have been applied for solving many complex problems. Generally, GAs work with a pool of candidate solutions (codified as a genome expression) via crossover and mutation mechanisms for generating new solution proposals for the problem. Algorithms differ in the customization of the genome representation of the solution for the problem, and in the fitness function used to evaluate the quality of solutions, based on the problem characteristics. In this paper an extension of the genetic algorithm itself is described. Using correspondents to the Mitosis and Meiosis processes for cell division, a framework for an extended genetic algorithm is developed. Numerical results with benchmark problems show that the solution quality obtained using the proposed algorithm is superior to that achieved by application of the original Genetic Algorithm. However, proposed GA couldn’t intelligently control the populations for the rate of Meiosis and Mitosis. ^
"A novel genetic algorithm and its application to optimize manufacturing schedules"
Dissertations and Master's Theses (Campus Access).