Coevolutionary genetic algorithm for constraint satisfaction with a genetic repair operator for effective schemata formation

We discuss a coevolutionary genetic algorithm for constraint satisfaction. Our basic idea is to explore effective genetic information in the population, i.e., schemata, and to exploit the genetic information in order to guide the population to better solutions. Our coevolutionary genetic algorithm (CGA) consists of two GA populations; the first GA, called “H-GA”, searches for the solutions in a given environment (problem), and the second GA, called “P-GA”, searches for effective genetic information involved in the H-GA, namely, good schemata. Thus, each individual in P-GA consists of alleles in H-GA or “don't care” symbol representing a schema in the H-GA. These GA populations separately evolve in each genetic space at different abstraction levels and affect with each other by two genetic operators: “superposition” and “transcription”. We then applied our CGA to constraint satisfaction problems (CSPs) incorporating a new stochastic “repair” operator for P-GA to raise the consistency of schemata with the (local) constraint conditions in CSPs. We carried out two experiments: First, we examined the performance of CGA on various “general” CSPs that are generated randomly for a wide variety of “density” and “tightness” of constraint conditions in the CSPs that are the basic measures of characterizing CSPs. Next, we examined “structured” CSPs involving latent “cluster” structures among the variables in the CSPs. For these experiments, computer simulations confirmed us the effectiveness of our CGA</p

Towards hybrid methods for solving hard combinatorial optimization problems

Tesis doctoral leída en la Escuela Politécnica Superior de la Universidad Autónoma de Madrid el 4 de septiembre de 200

Thermal and area optimization for component placement on PCB design using inverse genetic algorithm

Considering the current trend of compact designs which are mostly multiobjective in nature, proper arrangement of components has become a basic necessity so as to have optimal management of heat generation and dissipation. In this work, Inverse Genetic Algorithm (IGA) optimization has been adopted in order to achieve optimal placement of components on printed circuit board (PCB). The objective functions are the PCB area and temperature of each component while the constraint parameters are; to avoid the overlapping of components, the maximum allowable PCB area is 2(120193.4)mm2 , thermal connections were internally set, and the manufacturer allowable temperature for the ICs must be more than the components optimal temperature. In the conventional Forward Genetic Algorithm (FGA) optimization, the individual fitness of components are generated through the GA process. The IGA approach on the other hand, allows the user to set the desired fitness, so that the GA process will try to approach these set values. Hence, the IGA has two major advantages over FGA; the first being a reduction in the overall computational time and the other is the freedom of choosing the desired fitness (i.e. ability to manipulate the GA output). The objectives of this work includes; development of an IGA search Engine, minimization of the thermal profile of components based on thermal resistance network and the area of PCB, and comparison of the proposed IGA and FGA performances. From the simulation results, the IGA has successfully minimized the thermal profile and area of PCB by 0.78% and 1.28% respectively. The CPU-time has also been minimised by 15.56%

Solving constraint satisfaction problems with evolutionary algorithms

Eiben, A.E. [Promotor

Interactive narrative generation using computational verb theory

Interactive narrative extends traditional story-telling techniques by enabling previously passive observers to become active participants in the narrative events that unfold. A variety of approaches have attempted to construct such interactive narrative spaces and reconcile the goals of interactivity and dramatic story-telling. With the advent of the linguistic variable in 1972, a means was established for modelling natural language words and phrases mathematically and computationally. Over the past decade, the computational verb, first introduced in 1997, has been developed as a mathematical means of modelling natural language verbs in terms of dynamic systems, and vice versa. Computational verb theory extends the initial concept of the linguistic variable beyond being able to model adjectives, nouns, and passive states, into the realm of actions as denoted by natural language verbs. This thesis presents the framework and implementation of a system that generates interactive narrative spaces from narrative text. The concept of interactive narrative is introduced and recent developments in the area of interactive narrative are discussed. Secondly, a brief history of the development of the linguistic variable and the computational verb are provided. With the context of the computational verb (interactive) narrative generation (CVTNG) system presented, the underlying theoretical principles of the system are established. The CVTNG system principles are described in terms of fuzzy set, computational verb, and constraint satisfaction theory. The fuzzy set, computational verb, and constraint satisfaction principles are organised according to a CVTNG architecture. The CVTNG architecture is then described in terms of its subsystems, structures, algorithms, and interfaces. Each CVTNG system component is related to the overall design considerations and goals. A prototype of the CVTNG system is implemented and tested against a suite of natural language sentences. The behaviour and performance of the CVTNG system prototype are discussed in relation to the CVTNG system’s design principles. Results are calculated and stored as variable values that are dynamically and generically associated with representational means, specifically computer graphics, to illustrate the generation of interactive narrative spaces. Plans for future work are discussed to show the immense development potential of this application. The thesis concludes that the CVTNG system provides a solid and extendable base for the intuitive generation of interactive narrative spaces from narrative text, computational verb models, and freely associated media. CopyrightDissertation (MSc)--University of Pretoria, 2009.Computer ScienceUnrestricte

Evolutionary Search Techniques with Strong Heuristics for Multi-Objective Feature Selection in Software Product Lines

Software design is a process of trading off competing objectives. If the user objective space is rich, then we should use optimizers that can fully exploit that richness. For example, this study configures software product lines (expressed as feature models) using various search-based software engineering methods. Our main result is that as we increase the number of optimization objectives, the methods in widespread use (e.g. NSGA-II, SPEA2) perform much worse than IBEA (Indicator-Based Evolutionary Algorithm). IBEA works best since it makes most use of user preference knowledge. Hence it does better on the standard measures (hypervolume and spread) but it also generates far more products with 0 violations of domain constraints. We also present significant improvements to IBEA\u27s performance by employing three strong heuristic techniques that we call PUSH, PULL, and seeding. The PUSH technique forces the evolutionary search to respect certain rules and dependencies defined by the feature models, while the PULL technique gives higher weight to constraint satisfaction as an optimization objective and thus achieves a higher percentage of fully-compliant configurations within shorter runtimes. The seeding technique helps in guiding very large feature models to correct configurations very early in the optimization process. Our conclusion is that the methods we apply in search-based software engineering need to be carefully chosen, particularly when studying complex decision spaces with many optimization objectives. Also, we conclude that search methods must be customized to fit the problem at hand. Specifically, the evolutionary search must respect domain constraints