A Study of the Application of Chaos to the Genetic Algorithm
| dc.contributor.author | Jegede, Olawale | |
| dc.contributor.examiningcommittee | McLeod, Bob (Elec. & Comp. Eng.) Deng, Chuang (Mech. & Mfg. Eng.) | en_US |
| dc.contributor.supervisor | Ferens, Ken (Electrical and Computer Engineering) | en_US |
| dc.date.accessioned | 2014-04-10T14:28:33Z | |
| dc.date.available | 2014-04-10T14:28:33Z | |
| dc.date.issued | 2014-04-10 | |
| dc.degree.discipline | Electrical and Computer Engineering | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | This work focuses on the use of a genetic algorithm for optimization in a search-based problem. The Genetic Algorithm (GA) is a subset of evolutionary algorithms that models biological processes to optimize highly complex functions. A GA allows a population composed of many individuals to evolve under specified selection rules to a state that maximizes the “fitness” (i.e. minimize the objective function). A major advantage of using GA over most stochastic techniques is its parallelism, which speeds up the simulation results leading to faster convergence. With mutation, the GA is also less likely to get stuck in local minima compared to other stochastic techniques. However, some notable drawbacks of the Standard GA (SGA) include slow convergence and a possibility of being stuck in local optimum solution. The SGA uses a random process to generate parameter values for the initial population generation, crossover and mutation processes. Random number generators are designed to result in either uniform distributions or Gaussian distributions. We conjecture that the evolutionary processes in genetics are driven by a random non-linear deterministic dynamic process rather than a random non-deterministic process. Therefore, in the GA evolutionary process, a chaotic map is incorporated into the initial population generation, the crossover and mutation processes of the SGA; this is termed the Chaotic GA (CGA). The properties of a chaotic system that provides additional benefits over randomly generated solutions are sensitivity to initial conditions, topological density and topological transitivity (robust diversity). These properties ensure that the CGA is able to explore the entire solution space. Introducing chaos into the whole process of a standard genetic algorithm may help improve convergence time and accuracy. Simulation was done using Matlab and Java. | en_US |
| dc.description.note | May 2014 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/23428 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Genetic Algorithm | en_US |
| dc.subject | Chaotic Genetic Algorithm | en_US |
| dc.subject | Minimum EST | en_US |
| dc.subject | Maximum EST | en_US |
| dc.subject | Directed Acyclic Graph | en_US |
| dc.subject | Standard Genetic Algorithm | en_US |
| dc.title | A Study of the Application of Chaos to the Genetic Algorithm | en_US |
| dc.type | master thesis | en_US |