Improvements in genetic algorithms

Vasconcelos, J.A.; Ramírez, J.A.; Takahashi, Ricardo H. C.; Saldanha, R.R.

doi:10.1109/20.952626

Cited by 220 publications

(104 citation statements)

References 3 publications

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“…Circuits were represented by a binary genome with a fixed number of genes that encode NAND gates and one gene for each output. Each generation of the best L circuits passed unchanged to the next generation [elite strategy (28)]. Each circuit was randomly mutated (mutation probability P m ϭ 0.7 per genome).…”

Section: Methodsmentioning

confidence: 99%

Spontaneous evolution of modularity and network motifs

Kashtan

Alon²

2005

Proc. Natl. Acad. Sci. U.S.A.

779

803

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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. B iological and engineered systems share general design features: they display modularity, defined as the separability of the design into units that perform independently, at least to a first approximation (1-3, 5). † Furthermore, they show reuse of certain circuit patterns, termed network motifs (6-11), in many different parts of the system. These features allow construction of extremely complex systems by using simple building blocks (12).These features of biological networks are not captured by most current models of biological evolution. For example, many models of biological evolution use computers to evolve networks to attain a defined goal. In these simulations, networks in a population are varied by means of mutations, crossover, and duplication (13-15). Networks that perform better are selected in the next generation. A well known feature of these computational models of biological evolution is that the evolved systems are usually intricately wired and nonmodular. The nonmodular solutions are often more highly optimized than their human-engineered counterparts (16,17,20).The fundamental reason for the lack of modularity in these evolved networks is that modular structures are usually less optimal than nonmodular ones. Typically, there are many possible connections that break modularity and increase fitness. Thus, even an initially modular solution rapidly evolves into one of many possible nonmodular solutions.Lack of modularity is one of the reasons that computational evolution can currently generate designs for simple tasks, but has difficulty in scaling up to higher complexity. In the field of evolutionary design of engineered systems, approaches have been dev...

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Section: Methodsmentioning

confidence: 99%

Spontaneous evolution of modularity and network motifs

Kashtan

Alon²

2005

Proc. Natl. Acad. Sci. U.S.A.

779

803

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“…With regard to the algorithms in comparison, the settings for their specific parameters follow the same settings as described in the original papers [22][2] [21], as shown in Table 1. In this problem, the Griewangk function is scalable and the interactions among variables are nonlinear.…”

Section: Resultsmentioning

confidence: 99%

“…INGA improves the normal GA by dynamically changing the crossover and mutation probabilities [22]. There are two types of adaptation procedure as shown in Fig.…”

Section: Experimental Schemementioning

confidence: 99%

Incremental evolution strategy for function optimization

Guan

2007

HIS

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This paper presents a novel evolutionary approach for function optimization Incremental Evolution Strategy (IES). Two strategies are proposed. One is to evolve the input variables incrementally. The whole evolution consists of several phases and one more variable is focused in each phase. The number of phases is equal to the number of variables in maximum. Each phase is composed of two stages: in the single-variable evolution (SVE) stage, evolution is taken on one independent variable in a series of cutting planes; in the multi-variable evolving (MVE) stage, the initial population is formed by integrating the populations obtained by the SVE and the MVE in the last phase. And the evolution is taken on the incremented variable set.The other strategy is a hybrid of particle swarm optimization (PSO) and evolution strategy (ES). PSO is applied to adjust the cutting planes/hyper-planes (in SVEs/MVEs) while (1+1)-ES is applied to searching optima in the cutting planes/hyper-planes. The results of experiments show that the performance of IES is generally better than that of three other evolutionary algorithms, improved normal GA, PSO and SADE_CERAF, in the sense that IES finds solutions closer to the true optima and with more optimal objective values.

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“…v of each particle are assumed. These vectors can be determined randomly or predetermined in j-dimensional search space [23], where j is the number of design variables. In the next step the evaluation of each particle according to the objective function (called the fitness function) is carried out.…”

Section: The Particle Swarm Optimization Methodsmentioning

confidence: 99%

Application of a PSO algorithm for identification of the parameters of Jiles-Atherton hysteresis model

Knypiński¹,

Nowak²,

Sujka³

et al. 2012

Archives of Electrical Engineering

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Abstract:In the paper an algorithm and computer code for the identification of the hysteresis parameters of the Jiles-Atherton model have been presented. For the identification the particle swarm optimization method (PSO) has been applied. In the optimization procedure five design variables has been assumed. The computer code has been elaborated using Delphi environment. Three types of material have been examined. The results of optimization have been compared to experimental ones. Selected results of the calculation for different material are presented and discussed.

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Improvements in genetic algorithms

Cited by 220 publications

References 3 publications

Spontaneous evolution of modularity and network motifs

Spontaneous evolution of modularity and network motifs

Incremental evolution strategy for function optimization

Application of a PSO algorithm for identification of the parameters of Jiles-Atherton hysteresis model

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