The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

The Analysis of Time Series: An Introduction

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Linear And Nonlinear Programming

Biological networks have an inherent simplicity: they are modular with a design that can be separated into units that perform almost independently. Furthermore, they show reuse of recurring patterns termed network motifs. Little is known about the evolutionary origin of these properties. Current models of biological evolution typically produce networks that are highly nonmodular and lack understandable motifs. Here, we suggest a possible explanation for the origin of modularity and network motifs in biology. We use standard evolutionary algorithms to evolve networks. A key feature in this study is evolution under an environment (evolutionary goal) that changes in a modular fashion. That is, we repeatedly switch between several goals, each made of a different combination of subgoals. We find that such “modularly varying goals” lead to the spontaneous evolution of modular network structure and network motifs. The resulting networks rapidly evolve to satisfy each of the different goals. Such switching between related goals may represent biological evolution in a changing environment that requires different combinations of a set of basic biological functions. The present study may shed light on the evolutionary forces that promote structural simplicity in biological networks and offers ways to improve the evolutionary design of engineered systems.

/pdf/spontaneous-evolution-of-modularity-and-network-motifs-2u2ecjzipj.pdf

Spontaneous evolution of modularity and network motifs

In their original versions, nature-inspired search algorithms such as evolutionary algorithms and those based on swarm intelligence, lack a mechanism to deal with the constraints of a numerical optimization problem. Nowadays, however, there exists a considerable amount of research devoted to design techniques for handling constraints within a nature-inspired algorithm. This paper presents an analysis of the most relevant types of constraint-handling techniques that have been adopted with nature-inspired algorithms. From them, the most popular approaches are analyzed in more detail. For each of them, some representative instantiations are further discussed. In the last part of the paper, some of the future trends in the area, which have been only scarcely explored, are briefly discussed and then the conclusions of this paper are presented.

http://www.cs.cinvestav.mx/~constraint/papers/swevo-mezura.pdf

Constraint-Handling in Nature-Inspired Numerical Optimization: Past, Present and Future

This paper presents a multiobjective performance index-based size and location determination of distributed generation in distribution systems with different load models. Normally, a constant power (real and reactive) load model is assumed in most of the studies made in the literature. It is shown that load models can significantly affect the optimal location and sizing of distributed generation (DG) resources in distribution systems. The simulation technique based on genetic algorithms is studied. The studies have been carried out on 16-bus and 37-bus distribution systems.

Multiobjective Optimization for DG Planning With Load Models

This paper presents an exhaustive study of the Simple Genetic Algorithm (SGA), Steady State Genetic Algorithm (SSGA) and Replacement Genetic Algorithm (RGA). The performance of each method is analyzed in relation to several operators types of crossover, selection and mutation, as well as in relation to the probabilities of crossover and mutation with and without dynamic change of its values during the optimization process. In addition, the space reduction of the design variables and global elitism are analyzed. All GAs are effective when used with its best operations and values of parameters. For each GA, both sets of best operation types and parameters are found. The dynamic change of crossover and mutation probabilities, the space reduction and the global elitism during the evolution process show that great improvement can be achieved for all GA types. These GAs are applied to TEAM benchmark problem 22.

Improvements in genetic algorithms

This paper presents a multiobjective approach for the design of electrical distribution networks. The objectives are defined as a monetary cost index (including installation cost and energy losses cost) and a system failure index. The true Pareto-optimal solutions are found with a multiobjective genetic algorithm that employs an efficient variable encoding scheme and some problem-specific mutation and crossover operators. Results based on 21- and 100-bus systems are presented. The information gained from the Pareto-optimal solution set is shown to be useful for the decision-making stage of distribution network evolution planning.

Electric distribution network multiobjective design using a problem-specific genetic algorithm

Inverse problem methodology is extended, through the more difficult geometric differentiation of finite-element matrices, to identify the location, material, and value of unknown sources within an inaccessible region using exterior measurements. This is done through the definition of an object function that vanishes at its minimum when the externally measured electric field matches the electric field given by an assumed configuration that is optimized to match measurements. The method is demonstrated by identifying the shape, permittivity, charge, and location of an electrostatic source through exterior measurement. The procedure is then extended to eddy current problems for the identification of the location and shape of cracks in metallic structures. An example demonstrates that when dealing with eddy current problems the least squares object function used by others has multiple local minima so that gradient methods have to be combined with search methods to identify the one absolute minimum. Procedures are also given for handling situations with no cracks and overdescribed cracks. >

Inverse problem methodology and finite elements in the identification of cracks, sources, materials, and their geometry in inaccessible locations

Improving generalization of MLPs with multi-objective optimization

This paper presents a multi-objective evolutionary algorithm based on decomposition (MOEA/D) to design broadband optimal Yagi-Uda antennas. A multi-objective problem is formulated to achieve maximum directivity, minimum voltage standing wave ratio and maximum front-to-back ratio. The algorithm was applied to the design of optimal 3 to 10 elements Yagi-Uda antennas, whose optimal Pareto fronts are provided in a single picture. The multi-objective problem is decomposed by Chebyshev decomposition, and it is solved by differential evolution (DE) and Gaussian mutation operators in order to provide a better approximation of the Pareto front. The results show that the implemented MOEA/D is efficient for designing Yagi-Uda antennas.

/pdf/a-multi-objective-evolutionary-algorithm-based-on-1tsvxy0okt.pdf

Rodney R. Saldanha

Papers

Improvements in genetic algorithms

Electric distribution network multiobjective design using a problem-specific genetic algorithm

Inverse problem methodology and finite elements in the identification of cracks, sources, materials, and their geometry in inaccessible locations

Improving generalization of MLPs with multi-objective optimization

A Multi-Objective Evolutionary Algorithm Based on Decomposition for Optimal Design of Yagi-Uda Antennas