Protein Sequencing with an Adaptive Genetic Algorithm from Tandem Mass Spectrometry

In Proteomics, only the de novo peptide sequencing approach allows a partial amino acid sequence of a peptide to be found from a MS/MS spectrum. In this article a preliminary work is presented to discover a complete protein sequence from spectral data (MS and MS/MS spectra). For the moment, our approach only uses MS spectra. A Genetic Algorithm (GA) has been designed with a new evaluation function which works directly with a complete MS spectrum as input and not with a mass list like the other methods using this kind of data. Thus the mono isotopic peak extraction step which needs a human intervention is deleted. The goal of this approach is to discover the sequence of unknown proteins and to allow a better understanding of the differences between experimental proteins and proteins from databases

Making and breaking power laws in evolutionary algorithm population dynamics

Deepening our understanding of the characteristics and behaviors of population-based search algorithms remains an important ongoing challenge in Evolutionary Computation. To date however, most studies of Evolutionary Algorithms have only been able to take place within tightly restricted experimental conditions. For instance, many analytical methods can only be applied to canonical algorithmic forms or can only evaluate evolution over simple test functions. Analysis of EA behavior under more complex conditions is needed to broaden our understanding of this population-based search process. This paper presents an approach to analyzing EA behavior that can be applied to a diverse range of algorithm designs and environmental conditions. The approach is based on evaluating an individual’s impact on population dynamics using metrics derived from genealogical graphs.\ud From experiments conducted over a broad range of conditions, some important conclusions are drawn in this study. First, it is determined that very few individuals in an EA population have a significant influence on future population dynamics with the impact size fitting a power law distribution. The power law distribution indicates there is a non-negligible probability that single individuals will dominate the entire population, irrespective of population size. Two EA design features are however found to cause strong changes to this aspect of EA behavior: i) the population topology and ii) the introduction of completely new individuals. If the EA population topology has a long path length or if new (i.e. historically uncoupled) individuals are continually inserted into the population, then power law deviations are observed for large impact sizes. It is concluded that such EA designs can not be dominated by a small number of individuals and hence should theoretically be capable of exhibiting higher degrees of parallel search behavior

Optimization of touristic distribution netwoorks using genetic algorithms

The eight basic elements to design genetic algorithms (GA) are described and applied to solve a low demand distribution problem of passengers for a hub airport in Alicante and 30 touristic destinations in Northern Africa and Western Europe. The flexibility of GA and the possibility of creating mutually beneficial feed-back processes with human intelligence to solve complex problems as well as the difficulties in detecting erroneous codes embedded in the software are described. A new three-parent edge mapped recombination operator is used to solve the capacitated vehicle routing problem required for estimating associated costs with touristic distribution networks of low demand. GA proved to be very flexible especially in changing business environments and to solve decision-making problems involving ambiguous and sometimes contradictory constraints.Peer Reviewe

Optimization of touristic distribution netwoorks using genetic algorithms

The eight basic elements to design genetic algorithms (GA) are described and applied to solve a low demand distribution problem of passengers for a hub airport in Alicante and 30 touristic destinations in Northern Africa and Western Europe. The flexibility of GA and the possibility of creating mutually beneficial feed-back processes with human intelligence to solve complex problems as well as the difficulties in detecting erroneous codes embedded in the software are described. A new three-parent edge mapped recombination operator is used to solve the capacitated vehicle routing problem required for estimating associated costs with touristic distribution networks of low demand. GA proved to be very flexible especially in changing business environments and to solve decision-making problems involving ambiguous and sometimes contradictory constraints

Lunar Habitat Optimization Using Genetic Algorithms

Long-duration surface missions to the Moon and Mars will require bases to accommodate habitats for the astronauts. Transporting the materials and equipment required to build the necessary habitats is costly and difficult. The materials chosen for the habitat walls play a direct role in protection against each of the mentioned hazards. Choosing the best materials, their configuration, and the amount required is extremely difficult due to the immense size of the design region. Clearly, an optimization method is warranted for habitat wall design. Standard optimization techniques are not suitable for problems with such large search spaces; therefore, a habitat wall design tool utilizing genetic algorithms (GAs) has been developed. GAs use a "survival of the fittest" philosophy where the most fit individuals are more likely to survive and reproduce. This habitat design optimization tool is a multiobjective formulation of up-mass, heat loss, structural analysis, meteoroid impact protection, and radiation protection. This Technical Publication presents the research and development of this tool as well as a technique for finding the optimal GA search parameters

An adaptive parallel genetic algorithm.

Chi Wai Ho, Raymond.Thesis submitted in: December 1999.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 93-97).Abstracts in English and Chinese.Chapter Chapter 1 --- Introduction --- p.7Chapter 1.1 --- Thesis Outline --- p.10Chapter 1.2 --- Contribution at a Glance --- p.11Chapter Chapter 2 --- Background Concept and Related Work --- p.14Chapter 2.1 --- Genetic Algorithms (GAs) --- p.14Chapter 2.2 --- The Nature of GAs --- p.16Chapter 2.3 --- The Role of Mutation --- p.17Chapter 2.4 --- The Role of Crossover --- p.18Chapter 2.5 --- The Roles of the Mutation and Crossover Rates --- p.19Chapter 2.6 --- Adaptation of the Mutation and Crossover Rates --- p.19Chapter 2.7 --- Diversity Control --- p.21Chapter 2.8 --- Coarse-grain Parallel Genetic Algorithms --- p.25Chapter 2.9 --- Adaptation of Migration Period --- p.26Chapter 2.10 --- Serial and Parallel GAs --- p.27Chapter 2.11 --- Distributed Java Machine (DJM) --- p.28Chapter 2.12 --- Clustering --- p.30Chapter Chapter 3 --- Adaptation of the Mutation and Crossover Rates --- p.35Chapter 3.1 --- The Probabilistic Rule-based Adaptive Model (PRAM) --- p.35Chapter 3.2 --- Time Complexity --- p.37Chapter 3.3 --- Storage Complexity --- p.38Chapter Chapter 4 --- Diversity Control --- p.39Chapter 4.1 --- Repelling --- p.39Chapter 4.2 --- Implementation --- p.42Chapter 4.3 --- Lazy Repelling --- p.43Chapter 4.4 --- Repelling and Lazy Repelling with Deterministic Crowding --- p.43Chapter 4.5 --- Comparison of Repelling and Lazy Repelling with Recent Diversity Maintenance Models in Time Complexity --- p.44Chapter Chapter 5 --- An Adaptive Parallel Genetic Algorithm --- p.46Chapter 5.1 --- A Steady-State Genetic Algorithm --- p.46Chapter 5.2 --- An Adaptive Parallel Genetic Algorithm (aPGA) --- p.47Chapter 5.3 --- An Adaptive Parallel Genetic Algorithm for Clustering --- p.48Chapter 5.4 --- Implementation --- p.48Chapter 5.5 --- Time Complexity --- p.51Chapter Chapter 6 --- Performance Evaluation of PRAM --- p.52Chapter 6.1 --- Solution Quality --- p.58Chapter 6.2 --- Efficiency --- p.60Chapter 6.3 --- Discussion --- p.62Chapter Chapter 7 --- Performance Evaluation of Repelling --- p.66Chapter 7.1 --- Performance Comparison of Repelling and Lazy Repelling with Deterministic Crowding --- p.70Chapter 7.2 --- Performance Comparison with Recent Diversity Maintenance Models --- p.73Chapter 7.3 --- Performance Comparison with Serial and Parallel Gas --- p.75Chapter Chapter 8 --- Performance Evaluation of aPGA --- p.78Chapter 8.1 --- Scalability of Different Dimensionalities --- p.78Chapter 8.2 --- Speedup of Schwefel's function --- p.83Chapter 8.3 --- Solution Quality of Clustering Problems --- p.87Chapter 8.4 --- Speedup of The Clustering Problem --- p.89Chapter Chapter 9 --- Conclusion --- p.9

Optimización en el diseño de torres

Fecha de lectura de Tesis Doctoral: 24 de septiembre de 2003.En este trabajo se presenta la formalización de las técnicas de optimización sobre un sistema de cálculo, diseño y fabricación de torres metálicas de transmisión eléctrica y telecomunicación. Su desarrollo e implementación combina distintas herramientas matemáticas tales como el método de los elementos finitos, algoritmos de optimización o la resolución de grandes sistemas de ecuaciones, así como las correspondientes técnicas de ingeniería del software. Entre sus resultados cabe destacar la codificación de los distintos tipos de torres, la creación de las estructuras de datos necesarias para su modelización, la aplicación del sistema de algoritmos genéticos para el diseño óptimo de torres, la generación automática de los planos de fabricación, la minimización de residuos industriales y la definición del cálculo inverso a partir de las ecuaciones de y utilización. Todo ello se concreta en la creación de un sistema experto para el diseño global de estas estructuras, habiéndose contrastado sus resultados con el desarrollo y ensayo a escala real de diferentes prototipos de torres de acero. Junto a los resultados teóricos se adjuntan un conjunto de patentes internacionales surgido a partir de los modelos resultantes