An investigation of messy genetic algorithms

Genetic algorithms (GAs) are search procedures based on the mechanics of natural selection and natural genetics. They combine the use of string codings or artificial chromosomes and populations with the selective and juxtapositional power of reproduction and recombination to motivate a surprisingly powerful search heuristic in many problems. Despite their empirical success, there has been a long standing objection to the use of GAs in arbitrarily difficult problems. A new approach was launched. Results to a 30-bit, order-three-deception problem were obtained using a new type of genetic algorithm called a messy genetic algorithm (mGAs). Messy genetic algorithms combine the use of variable-length strings, a two-phase selection scheme, and messy genetic operators to effect a solution to the fixed-coding problem of standard simple GAs. The results of the study of mGAs in problems with nonuniform subfunction scale and size are presented. The mGA approach is summarized, both its operation and the theory of its use. Experiments on problems of varying scale, varying building-block size, and combined varying scale and size are presented

FEM Mesh Mapping to a SIMD Machine Using Genetic Algorithms

The Finite Element Method is a computationally expensive method used to perform engineering analyses. By performing such computations on a parallel machine using a SIMD paradigm, these analyses\u27 run time can be drastically reduced. However, the mapping of the FEM mesh elements to the SIMD machine processing elements is an NP-complete problem. This thesis examines the use of Genetic Algorithms as a search technique to find quality solutions to the mapping problem. A hill climbing algorithm is compared to a traditional genetic algorithm, as well as a messy genetic algorithm. The results and comparative advantages of these approaches are discussed

Optimized Micro-Hydro Power Plants Layout Design Using Messy Genetic Algorithms

Micro Hydro-Power Plants (MHPP) represent a powerful and effective solution to address the problem of energy poverty in rural remote areas, with the ad vantage of preserving the natural resources and minimizing the impact on the environment. Nevertheless, the lack of resources and qualified manpower usu ally constitutes a big obstacle to its adequate application, generally translating into sub-optimal generation systems with poor levels of efficiency. Therefore, the study and development of expert, simple and efficient strategies to assist the design of these installations is of especial relevance. This work proposes a design methodology based on a tailored messy evolutionary computational approach, with the objective of finding the most suitable layout of MHPP, considering several constraints derived from a minimal power supply requirement, the max imum flow usage, and the physical feasibility of the plant in accordance with the real terrain profile. This profile is built on the basis of a discrete topographic survey, by means of a shape preserving interpolation, which permits the appli cation of a continuous variable length Messy Genetic Algorithm (MGA). The optimization problem is then formulated in both single-objective (cost minimiza tion) and multi-objective (cost minimization and power supply maximization) modes, including the study of the Pareto dominance. The algorithm is applied to a real scenario in a remote community in Honduras, obtaining a 56.96% of cost reduction with respect to previous work

A Multiobjective Approach Applied to the Protein Structure Prediction Problem

Interest in discovering a methodology for solving the Protein Structure Prediction problem extends into many fields of study including biochemistry, medicine, biology, and numerous engineering and science disciplines. Experimental approaches, such as, x-ray crystallographic studies or solution Nuclear Magnetic Resonance Spectroscopy, to mathematical modeling, such as minimum energy models are used to solve this problem. Recently, Evolutionary Algorithm studies at the Air Force Institute of Technology include the following: Simple Genetic Algorithm (GA), messy GA, fast messy GA, and Linkage Learning GA, as approaches for potential protein energy minimization. Prepackaged software like GENOCOP, GENESIS, and mGA are in use to facilitate experimentation of these techniques. In addition to this software, a parallelized version of the fmGA, the so-called parallel fast messy GA, is found to be good at finding semi-optimal answers in reasonable wall clock time. The aim of this work is to apply a Multiobjective approach to solving this problem using a modified fast messy GA. By dividing the CHARMm energy model into separate objectives, it should be possible to find structural configurations of a protein that yield lower energy values and ultimately more correct conformations

Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using discrete and fuzzy dynamical system representations within the XCSF learning classifier system. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules in the discrete case and asynchronous fuzzy logic networks in the continuous-valued case. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such dynamical systems within XCSF to solve a number of well-known test problems