Evolutionary search techniques application in automated layout-planning optimization problem

2002 ◽  
Author(s):  
C. Bounsaythip ◽  
S. Maouche ◽  
M. Neus
Keyword(s):  
Optimization Problem ◽  
Evolutionary Search ◽  
Layout Planning ◽  
Search Techniques ◽  
Planning Optimization

Combinatorics of Genotype-Phenotype Maps: An RNA Case Study

2005 ◽  
Author(s):  
Christian M. Reidys
Keyword(s):  
Natural Selection ◽  
Genetic Drift ◽  
Optimization Problem ◽  
Neutral Theory ◽  
Biological Evolution ◽  
Neutral Evolution ◽  
Evolutionary Search ◽  
Random Drift ◽  
Molecular Change ◽  
Biological Phenomena

The fundamental mechanisms of biological evolution have fascinated generations of researchers and remain popular to this day. The formulation of such a theory goes back to Darwin (1859), who in the The Origin of Species presented two fundamental principles: genetic variability caused by mutation, and natural selection. The first principle leads to diversity and the second one to the concept of survival of the fittest, where fitness is an inherited characteristic property of an individual and can basically be identified with its reproduction rate. Wright [530, 531] first recognized the importance of genetic drift in evolution in improving the evolutionary search capacity of the whole population. He viewed genetic drift merely as a process that could improve evolutionary search. About a decade later, Kimura proposed [317] that the majority of changes that are observed in evolution at the molecular level are the results of random drift of genotypes. The neutral theory of Kimura does not deny that selection plays a role, but claims that no appreciable fraction of observable molecular change can be caused by selective forces: mutations are either a disadvantage or, at best, neutral in present day organisms. Only negative selection plays a major role in the neutral evolution, in that deleterious mutants die out due to their lower fitness. Over the last few decades, there has been a shift of emphasis in the study of evolution. Instead of focusing on the differences in the selective value of mutants and on population genetics, interest has moved to evolution through natural selection as an abstract optimization problem. Given the tremendous opportunities that computer science and the physical sciences now have for contributing to the study of biological phenomena, it is fitting to study the evolutionary optimization problem in the present volume. In this chapter, we adopt the following framework: assuming that selection acts exclusively upon isolated phenotypes, we introduce the following compositum of mappings . . . Genotypes→ Phenotypes →Fitness . . . . We will refer to the first map as to the genotype-phenotype map and call the preimage of a given phenotype its neutral network. Clearly, the main ingredients here are the phenotypes and genotypes and their respective organization. In the following we will study various combinatorial properties of phenotypes and genotypes for RNA folding maps.

Production Planning Optimization in F Company: A Scheduling Theory Case Study

2013 ◽  
Vol 655-657 ◽  
pp. 1646-1649 ◽  
Author(s):  
Chi Zhang ◽  
Zhen He ◽  
Yuan Peng Ruan
Keyword(s):  
Linear Programming ◽  
Production Planning ◽  
Optimization Problem ◽  
Assembly Line ◽  
Scheduling Theory ◽  
Scheduling Model ◽  
Planning Optimization ◽  
Optimization Methodology ◽  
Optimum Production

Scheduling is an effective optimization methodology which has been widely used for production planning. This paper presents a scheduling model to optimize the output of an assembly line in F Semiconductor Company in Tianjin, China. The authors formulate the optimization problem as linear programming. The model and its implementation are described in detail in this article. The optimum production allocations have been founded by the scheduling model and the output has been increased.

Multiple Lagrange Multiplier Method for Constrained Evolutionary Optimization

2000 ◽  
Vol 4 (2) ◽  
pp. 158-163 ◽  
Author(s):  
Hyun Myung ◽  
◽  
Jong-Hwan Kim ◽  
Keyword(s):  
Lagrange Multiplier ◽  
Optimization Problem ◽  
Evolutionary Programming ◽  
Optimization Techniques ◽  
Lagrange Multiplier Method ◽  
Multiplier Method ◽  
Evolutionary Search ◽  
Two Phase ◽  
Global Optima ◽  
Sharing Function

One of the well-known problems in evolutionary search for solving optimization problem is the premature convergence. The general constrained optimization techniques such as hybrid evolutionary programming, two-phase evolutionary programming, and Evolian algorithms are not safe from the same problem in the first phase. To overcome this problem, we apply the sharing function to the Evolian algorithm and propose to use the multiple Lagrange multiplier method for the subsequent phases of Evolian. The method develops Lagrange multipliers in each subpopulation region independently and seeks for multiple global optima, if any, in parallel. The simulation results demonstrate the usefulness of the proposed multiple Lagrange multiplier method.

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