Using Evolutionary Algorithms to Generate Alternatives for Multiobjective Site-Search Problems

2002 ◽  
Vol 34 (4) ◽  
pp. 639-656 ◽  
Author(s):  
Ningchuan Xiao ◽  
David A Bennett ◽  
Marc P Armstrong
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Decision Problems ◽  
Graph Representation ◽  
Search Problems ◽  
Site Search ◽  
Conflicting Objectives ◽  
Different Types ◽  
Critical Issues ◽  
A Site

Multiobjective site-search problems are a class of decision problems that have geographical components and multiple, often conflicting, objectives; this kind of problem is often encountered and is technically difficult to solve. In this paper we describe an evolutionary algorithm (EA) based approach that can be used to address such problems. We first describe the general design of EAs that can be used to generate alternatives that are optimal or close to optimal with respect to multiple criteria. Then we define the problem addressed in this research and discuss how the EA was designed to solve it. In this procedure, called MOEA/Site, a solution (that is, a site) is encoded by using a graph representation that is operated on by a set of specifically designed evolutionary operations. This approach is applied to five different types of cost surfaces and the results are compared with 10 000 randomly generated solutions. The results demonstrate the robustness and effectiveness of this EA-based approach to geographical analysis and multiobjective decisionmaking. Critical issues regarding the representation of spatial solutions and associated evolutionary operations are also discussed.

scholarly journals Drift Analysis and Evolutionary Algorithms Revisited

2018 ◽  
Vol 27 (4) ◽  
pp. 643-666 ◽  
Author(s):  
J. LENGLER ◽  
A. STEGER
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Random Search ◽  
Optimization Algorithms ◽  
Simple Algorithm ◽  
Linear Functions ◽  
Search Point ◽  
Drift Analysis ◽  
Fixed Constant ◽  
Greedy Optimization

One of the easiest randomized greedy optimization algorithms is the following evolutionary algorithm which aims at maximizing a function f: {0,1}n → ℝ. The algorithm starts with a random search point ξ ∈ {0,1}n, and in each round it flips each bit of ξ with probability c/n independently at random, where c > 0 is a fixed constant. The thus created offspring ξ' replaces ξ if and only if f(ξ') ≥ f(ξ). The analysis of the runtime of this simple algorithm for monotone and for linear functions turned out to be highly non-trivial. In this paper we review known results and provide new and self-contained proofs of partly stronger results.

scholarly journals An automated system analysis and design tool for spacecrafts ceas space journal

2021 ◽  
Author(s):  
Manfred Ehresmann ◽  
Georg Herdrich ◽  
Stefanos Fasoulas
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Degrees Of Freedom ◽  
System Analysis ◽  
Propulsion System ◽  
Optimal System ◽  
Automated System ◽  
Processing Unit ◽  
Data Set ◽  
Analysis And Design

AbstractIn this paper, a generic full-system estimation software tool is introduced and applied to a data set of actual flight missions to derive a heuristic for system composition for mass and power ratios of considered sub-systems. The capability of evolutionary algorithms to analyse and effectively design spacecraft (sub-)systems is shown. After deriving top-level estimates for each spacecraft sub-system based on heuristic heritage data, a detailed component-based system analysis follows. Various degrees of freedom exist for a hardware-based sub-system design; these are to be resolved via an evolutionary algorithm to determine an optimal system configuration. A propulsion system implementation for a small satellite test case will serve as a reference example of the implemented algorithm application. The propulsion system includes thruster, power processing unit, tank, propellant and general power supply system masses and power consumptions. Relevant performance parameters such as desired thrust, effective exhaust velocity, utilised propellant, and the propulsion type are considered as degrees of freedom. An evolutionary algorithm is applied to the propulsion system scaling model to demonstrate that such evolutionary algorithms are capable of bypassing complex multidimensional design optimisation problems. An evolutionary algorithm is an algorithm that uses a heuristic to change input parameters and a defined selection criterion (e.g., mass fraction of the system) on an optimisation function to refine solutions successively. With sufficient generations and, thereby, iterations of design points, local optima are determined. Using mitigation methods and a sufficient number of seed points, a global optimal system configurations can be found.

Evolutionary Algorithms In Architecture

2011 ◽  
Vol 36 (1) ◽  
pp. 16-24
Author(s):  
Peter Schwehr
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Everyday Life ◽  
Working Model ◽  
Constant Change ◽  
Buckminster Fuller ◽  
High Level

Change is a reliable constant. Constant change calls for strategies in managing everyday life and a high level of flexibility. Architecture must also rise to this challenge. The architect Richard Buckminster Fuller claimed that “A room should not be fixed, should not create a static mood, but should lend itself to change so that its occupants may play upon it as they would upon a piano (Krausse 2001).” This liberal interpretation in architecture defines the ability of a building to react to (ever-) changing requirements. The aim of the project is to investigate the flexibility of buildings using evolutionary algorithms characterized by Darwin. As a working model for development, the evolutionary algorithm consists of variation, selection and reproduction (VSR algorithm). The result of a VSR algorithm is adaptability (Buskes 2008). If this working model is applied to architecture, it is possible to examine as to what extent the adaptability of buildings – as an expression of a cultural achievement – is subject to evolutionary principles, and in which area the model seems unsuitable for the 'open buildings' criteria. (N. John Habraken). It illustrates the significance of variation, selection and replication in architecture and how evolutionary principles can be transferred to the issues of flexible buildings. What are the consequences for the building if it were to be designed and built with the help of evolutionary principles? How can we react to the growing demand for flexibilization of buildings by using evolutionary principles?

scholarly journals Serous Tubal Intraepithelial Carcinoma or Not? Metastases to Fallopian Tube Mucosa Can Masquerade as In Situ Lesions

2017 ◽  
Vol 141 (10) ◽  
pp. 1313-1315 ◽  
Author(s):  
Reena Singh ◽  
Kathleen R. Cho
Keyword(s):  
Fallopian Tube ◽  
De Novo ◽  
Data Sources ◽  
High Grade ◽  
Serous Tubal Intraepithelial Carcinoma ◽  
Different Types ◽  
Intraepithelial Carcinoma ◽  
A Site ◽  
Immunohistochemical Stains

Context.— Nonuterine high-grade serous carcinomas (HGSCs) are believed to arise most often from precursors in the fallopian tube referred to as serous tubal intraepithelial carcinomas (STICs). A designation of tubal origin has been suggested for all cases of nonuterine HGSC if a STIC is identified. Objective.— To highlight that many different types of nongynecologic and gynecologic carcinomas, including HGSC, can metastasize to the tubal mucosa and mimic de novo STIC. Data Sources.— A mini-review of several recently published studies that collectively examine STIC-like lesions of the fallopian tube. Conclusions.— The fallopian tube mucosa can be a site of metastasis from carcinomas arising elsewhere, and pathologists should exercise caution in diagnosing STIC without first considering the possibility of metastasis. Routinely used immunohistochemical stains can often be used to determine if a STIC-like lesion is tubal or nongynecologic in origin. In the context of uterine and nonuterine HGSC, STIC may represent a metastasis rather than the site of origin, particularly when widespread disease is present.

scholarly journals A new sex factor ofPseudomonas aeruginosa

1973 ◽  
Vol 21 (3) ◽  
pp. 263-272 ◽  
Author(s):  
J. M. Pemberton ◽  
B. W. Holloway
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Structural Gene ◽  
Suppressor Gene ◽  
Genetic Determinant ◽  
Sex Factors ◽  
Wild Type ◽  
Leucine Biosynthesis ◽  
Different Types ◽  
A Site ◽  
Type Strains

SUMMARYOf 150 wild-type strains ofPseudomonas aeruginosaexamined, 48 formed recombinants when mated toP. aeruginosastrain PAO FP−and hence presumably possess sex factors. Three different types of sex factor were distinguished by the pattern of transfer of particular markers in different regions of the chromosome and by the ability to confer resistance to mercury in strain PAO. One new sex factor, FP39, was studied in detail, and while similar to the previously studied FP2 in terms of transfer kinetics, natural stability and resistance to curing by acridines, it differed from FP2 in promoting chromosome transfer from a site 10 min to the left of the FP2 origin and in showing apparently aberrant entry kinetics for a leucine marker situated 48 min from the FP2 origin. This was due to FP39 having a genetic determinant either for a structural gene of leucine biosynthesis or a specific suppressor gene for this locus. PAO strains carrying both FP2 and FP39 were unstable for both sex factors, suggesting a relationship between them.

scholarly journals Heliostat Field Layout Optimization with Evolutionary Algorithms

10.29007/7p6t ◽  
2018 ◽  
Author(s):  
Pascal Richter ◽  
David Laukamp ◽  
Levin Gerdes ◽  
Martin Frank ◽  
Erika Ábrahám
Keyword(s):  
Power Plant ◽  
Convex Optimization ◽  
Evolutionary Algorithms ◽  
Convergence Rate ◽  
Evolutionary Algorithm ◽  
Computer Science ◽  
Energy Supply ◽  
Optimization Problem ◽  
Convex Optimization Problem ◽  
New Genotype

The exploitation of solar power for energy supply is of increasing importance. While technical development mainly takes place in the engineering disciplines, computer science offers adequate techniques for optimization. This work addresses the problem of finding an optimal heliostat field arrangement for a solar tower power plant.We propose a solution to this global, non-convex optimization problem by using an evolutionary algorithm. We show that the convergence rate of a conventional evolutionary algorithm is too slow, such that modifications of the recombination and mutation need to be tailored to the problem. This is achieved with a new genotype representation of the individuals.Experimental results show the applicability of our approach.

scholarly journals Implementation of new hybrid evolutionary algorithm with fuzzy logic control approach for optimization problems

2021 ◽  
Vol 6 (4 (114)) ◽  
pp. 6-14
Author(s):  
Maan Afathi
Keyword(s):  
Fuzzy Logic ◽  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Fuzzy Logic Control ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Function Optimization ◽  
Evolutionary Computations ◽  
Hybrid Evolutionary Algorithm ◽  
Logic Control

The main purpose of using the hybrid evolutionary algorithm is to reach optimal values and achieve goals that traditional methods cannot reach and because there are different evolutionary computations, each of them has different advantages and capabilities. Therefore, researchers integrate more than one algorithm into a hybrid form to increase the ability of these algorithms to perform evolutionary computation when working alone. In this paper, we propose a new algorithm for hybrid genetic algorithm (GA) and particle swarm optimization (PSO) with fuzzy logic control (FLC) approach for function optimization. Fuzzy logic is applied to switch dynamically between evolutionary algorithms, in an attempt to improve the algorithm performance. The HEF hybrid evolutionary algorithms are compared to GA, PSO, GAPSO, and PSOGA. The comparison uses a variety of measurement functions. In addition to strongly convex functions, these functions can be uniformly distributed or not, and are valuable for evaluating our approach. Iterations of 500, 1000, and 1500 were used for each function. The HEF algorithm’s efficiency was tested on four functions. The new algorithm is often the best solution, HEF accounted for 75 % of all the tests. This method is superior to conventional methods in terms of efficiency

Genetic Diversity as an Objective in Multi-Objective Evolutionary Algorithms

2003 ◽  
Vol 11 (2) ◽  
pp. 151-167 ◽  
Author(s):  
Andrea Toffolo ◽  
Ernesto Benini
Keyword(s):  
Genetic Diversity ◽  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Selection Pressure ◽  
Evaluation Method ◽  
State Of The Art ◽  
Search Space ◽  
Test Problems ◽  
Multi Objective ◽  
Diversity Evaluation

A key feature of an efficient and reliable multi-objective evolutionary algorithm is the ability to maintain genetic diversity within a population of solutions. In this paper, we present a new diversity-preserving mechanism, the Genetic Diversity Evaluation Method (GeDEM), which considers a distance-based measure of genetic diversity as a real objective in fitness assignment. This provides a dual selection pressure towards the exploitation of current non-dominated solutions and the exploration of the search space. We also introduce a new multi-objective evolutionary algorithm, the Genetic Diversity Evolutionary Algorithm (GDEA), strictly designed around GeDEM and then we compare it with other state-of-the-art algorithms on a well-established suite of test problems. Experimental results clearly indicate that the performance of GDEA is top-level.

Differential Evolution Dynamic Analysis in the Form of Complex Networks

2016 ◽  
pp. 285-318 ◽  
Author(s):  
Lenka Skanderova ◽  
Ivan Zelinka
Keyword(s):  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Complex Networks ◽  
Dynamic Analysis ◽  
Evolutionary Algorithm ◽  
Complex Network ◽  
Novel Method

In this work, we investigate the dynamics of Differential Evolution (DE) using complex networks. In this pursuit, we would like to clarify the term complex network and analyze its properties briefly. This chapter presents a novel method for analysis of the dynamics of evolutionary algorithms in the form of complex networks. We discuss the analogy between individuals in populations in an arbitrary evolutionary algorithm and vertices of a complex network as well as between edges in a complex network and communication between individuals in a population. We also discuss the dynamics of the analysis.

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