Model Of Natural Evolution Research Articles

A selection method for evolutionary algorithms based on the Golden Section

During millions of years, nature has developed patterns and processes with interesting characteristics. They have been used as inspiration for a significant number of innovative models that can be extended to solve complex engineering and mathematical problems. One of the most famous patterns present in nature is the Golden Section (GS). It defines an especial proportion that allows the adequate formation, selection, partition, and replication in several natural phenomena. On the other hand, Evolutionary algorithms (EAs) are stochastic optimization methods based on the model of natural evolution. One important process in these schemes is the operation of selection which exerts a strong influence on the performance of their search strategy. Different selection methods have been reported in the literature. However, all of them present an unsatisfactory performance as a consequence of the deficient relations between elitism and diversity of their selection procedures. In this paper, a new selection method for evolutionary computation algorithms is introduced. In the proposed approach, the population is segmented into several groups. Each group involves a certain number of individuals and a probability to be selected, which are determined according to the GS proportion. Therefore, the individuals are divided into categories where each group contains individual with similar quality regarding their fitness values. Since the possibility to choose an element inside the group is the same, the probability of selecting an individual depends exclusively on the group from which it belongs. Under these conditions, the proposed approach defines a better balance between elitism and diversity of the selection strategy. Numerical simulations show that the proposed method achieves the best performance over other selection algorithms, in terms of its solution quality and convergence speed.

Transportation costs and urban sprawl in Canadian metropolitan areas

We conduct an econometric analysis of the potential impact of gasoline and parking prices on urban sprawl in ten Canadian metropolitan areas from 1996 to 2011. Two measures of urban sprawl related to density and proximity are used as dependent variables: the proportion of low-density housing and the median commute distance. We explain these measures by four main variables based on the natural evolution model: population growth, median household income, the cost of surrounding agricultural land, and transportation costs. We show that, ceteris paribus, higher parking and gasoline prices have contributed to reduce the extent of urban sprawl. On average, a 1% increase in gasoline prices has led to a decrease in low-density housing by 0.17% and to a 0.04% decrease in median commute distance. Furthermore, we show that a 1% increase in the price of off-street parking has led to a 0.12% decrease in low-density housing and to a 0.05% decrease in median commute distance. We argue that results for parking prices are relatively modest because much free parking is available.

Spacecraft thermal design with the Generalized Extremal Optimization Algorithm

This article describes an application of the Generalized Extremal Optimization (GEO) algorithm to the inverse design of a spacecraft thermal control system. GEO is a recently proposed global search meta-heuristic (Sousa, F.L. and Ramos, F.M., 2002, Function optimization using extremal dynamics. In: Proceedings of the 4th International Conference on Inverse Problems in Engineering (cd-rom), Rio de Janeiro, Brazil.; Sousa, F.L., Ramos, F.M., Paglione, P. and Girardi, R.M., 2003, New stochastic algorithm for design optimization. AIAA Journal, 41(9), 1808–1818.; Sousa, F.L., Ramos, F.M., Galski, R.L. and Muraoka, I., 2005, Chapter III. In: L.N. De Castro and F.J. Von Zuben (Eds) Generalized Extremal Optimization: A New Meta-heuristic Inspired by a Model of Natural Evolution, Accepted for publication in Recent Developments in Biologically Inspired Computing (Hershey, PA: Idea Group Inc.).) based on a model of natural evolution (Bak, P. and Sneppen, K., 1993, Punctuated equilibrium and criticality in a simple model of evolution. Physical Review Letters, 71(24), 4083–4086), and specially devised to be used in complex optimization problems (Sousa, F.L., Vlassov, V. and Ramos, F.M., 2002, Heat pipe design through generalized extremal optimization. In: Proceedings of the IX Brazilian Congress of Engineering and Thermal Sciences – ENCIT 2002, Caxambu, MG, Brazil.). GEO is easy to implement, has only one free parameter to adjust, does not make use of derivatives and can be applied to constrained or unconstrained problems, and to non-convex or even disjoint design spaces with any combination of continuous, discrete, or integer variables. The application reported here concerns the optimum design of a simplified configuration of the Brazilian Multi-mission Platform (in Portuguese, Plataforma Multi-missão, PMM) thermal control subsystem, comprising five radiators and one battery heater. The PMM is a multi-purpose space platform to be used in different types of missions such as Earth observation, scientific, or meteorological data collecting. The design procedure is tackled as a multiobjective optimization problem, considering two critical cases, operational hot case (HC) and cold case (CC). The results indicate the existence of non-intuitive, new and more efficient design solutions.

Optimal discrete truss design using improved sequential and genetic algorithm

The potential of two distinct approaches applied to the truss discrete optimization problem is presented in the paper. The sequential discrete optimization method SDO (which is a deterministic procedure, using heuristics based on the idea of fully stressed truss design) and the genetic algorithm GA (a stochastic search method, inspired by the natural evolution model) are compared. The minimum weight design of truss structures subjected to stress and displacement constraints is investigated, including the case of multiple load conditions. The discrete design variables are areas of members, selected from a finite catalogue of available sections. Benchmark 2D and 3D problems are presented in numerical examples. The effectiveness of two approaches is discussed. The improvements of both algorithms and GA integrating the results of SDO method are proposed. They enable us to accelerate the convergence, diminish the number of structural analyses and guide to refined “near optimal” solutions.

Evolutionary Programming that Utilizes Genetic Drift

Evolutionary Programming (EP) belongs to a class of general optimization algorithms based on the model of natural evolution. EP has also been applied to real-valued function optimization since the early 90's. However, recent research results have proved that EP is not so robust as expected; EP performs very well only when the lower bound of strategy parameters is adjusted to each problem. In order to overcome this difficulty, an extended EP, called Robust EP (REP), is proposed. A major feature of REP is that genetic drift is introduced as another source of changing strategy parameters. Computer simulations are conducted in order to illustrate the robust performance of REP against the lower bound on a set of popular benchmark problems. Some evolutionary characteristics of REP are also clarified by calculating basic statistical values.

Friction compensation control of an electropneumatic servovalve by using an evolutionary algorithm

A poppet-type electropneumatic servovalve developed in this study utilizes a poppet directly operated by a moving-coil actuator in the metering stage and is controlled by a digital controller. This servovalve is insensitive to air contamination and has no problem of air leakage at null, but it has relatively large friction between the O-rings installed in the peripheral grooves of the balance pistons and the valve sleeve. For friction compensation control, a static friction model that enables simulation of the stick-slip phenomena and a dynamic model that captures the friction behaviour such as presliding displacement and varying break-away force are presented. The parameters for the friction models are identified by utilizing an evolution strategy, one of the evolutionary algorithms, which is a probabilistic global search algorithm based on the model of natural evolution. These friction models are then used in designing a non-linear friction compensation controller. It is found in the experiment that the electropneumatic servovalve has almost no hysteresis and that the friction compensation control significantly improves valve performance. The experimental results of the open loop test on poppet positioning agree well with simulation results of the valve model with identified friction parameters. It is also shown that the experimental results of friction compensation control using a static friction model show a small steady state error but those using a dynamic friction model show almost no such error.

A Comparison of Selection Schemes Used in Evolutionary Algorithms

Evolutionary algorithms are a common probabilistic optimization method based on the model of natural evolution. One important operator in these algorithms is the selection scheme, for which in this paper a new description model, based on fitness distributions, is introduced. With this, a mathematical analysis of tournament selection, truncation selection, ranking selection, and exponential ranking selection is carried out that allows an exact prediction of the fitness values after selection. The correspondence of binary tournament selection and ranking selection in the expected fitness distribution is proved. Furthermore, several properties of selection schemes are derived (selection intensity, selection variance, loss of diversity), and the three selection schemes are compared using these properties.

Allocating data to distributed-memory multiprocessors by genetic algorithms

We present three genetic algorithms (GAs) for allocating irregular data sets to multiprocessors. These are a sequential hybrid GA, a coarse-grain GA and a fine-grain GA. The last two are based on models of natural evolution that are suitable for parallel implementation; they have been implemented on a hypercube and a Connection Machine. Experimental results show that the three GAs evolve good suboptimal solutions which are better than those produced by other methods. The GAs are also robust and do not show a bias towards particular problem configurations. The two parallel GAs have reasonable execution times, with the coarse-grain GA producing better solutions for the allocation of loosely synchronous computations.

An Overview of Evolutionary Algorithms for Parameter Optimization

Three main streams of evolutionary algorithms (EAs), probabilistic optimization algorithms based on the model of natural evolution, are compared in this article: evolution strategies (ESs), evolutionary programming (EP), and genetic algorithms (GAs). The comparison is performed with respect to certain characteristic components of EAs: the representation scheme of object variables, mutation, recombination, and the selection operator. Furthermore, each algorithm is formulated in a high-level notation as an instance of the general, unifying basic algorithm, and the fundamental theoretical results on the algorithms are presented. Finally, after presenting experimental results for three test functions representing a unimodal and a multimodal case as well as a step function with discontinuities, similarities and differences of the algorithms are elaborated, and some hints to open research questions are sketched.

Evolutionary algorithms

Genetic Algorithms and Evolution Strategies, the main representatives of a class of algorithms based on the model of natural evolution, are discussed w.r.t. their basic working mechanisms, differences, and application possibilities. The mechanism of self-adaptation of strategy parameters within Evolution Strategies is emphasized and turns out to be the major difference to Genetic Algorithms, since it allows for an on-line adaptation of strategy parameters without exogenous control. Furthermore, the importance of selection as a mechanism to control the character of the search to be either more volume oriented or more path oriented is pointed out.