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 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.

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 Strategy for Individuals Distribution by Incident Nodes Participation in Star Topology of Distributed Evolutionary Algorithms

2016 ◽  
Vol 16 (1) ◽  
pp. 80-88 ◽  
Author(s):  
Todor Balabanov ◽  
Iliyan Zankinski ◽  
Maria Barova
Keyword(s):  
Distributed Computing ◽  
Evolutionary Algorithms ◽  
Heterogeneous Computing ◽  
Local Population ◽  
Combinatorial Problem ◽  
Star Topology ◽  
Remote Computation ◽  
Distributed Evolutionary Algorithms ◽  
High Level ◽  
Communication Demands

Abstract One of the strongest advantages of Distributed Evolutionary Algorithms (DEAs) is that they can be implemented in distributed environment of heterogeneous computing nodes. Usually such computing nodes differ in hardware and operating systems. Distributed systems are limited by network latency. Some Evolutionary Algorithms (EAs) are quite suitable for distributed computing implementation, because of their high level of parallelism and relatively less intensive network communication demands. One of the most widely used topologies for distributed computing is the star topology. In a star topology there is a central node with global EA population and many remote computation nodes which are working on a local population (usually sub-population of the global population). This model of distributed computing is also known as island model. What is common for DEAs is an operation called migration that transfers some individuals between local populations. In this paper, the term 'distribution' will be used instead of the term 'migration', because it is more accurate for the model proposed. This research proposes a strategy for distribution of EAs individuals in star topology based on incident node participation (INP). Solving the Rubik's cube by a Genetic Algorithm (GA) will be used as a benchmark. It is a combinatorial problem and experiments are done with a C++ program which uses OpenMPI.

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.

Computer use by People with Aphasia: A Survey Investigation

2014 ◽  
Vol 15 (2) ◽  
pp. 107-119 ◽  
Author(s):  
Emma Finch ◽  
Anne J. Hill
Keyword(s):  
Everyday Life ◽  
Computer Use ◽  
Daily Life ◽  
Computer Usage ◽  
Clinical Environment ◽  
Aphasia Therapy ◽  
Before And After ◽  
Computer Based ◽  
Positive Experiences ◽  
High Level

Computers are encountered increasingly in the clinical setting, including during aphasia rehabilitation. However, currently we do not know what people with aphasia think about using computers in therapy and daily life, or to what extent people with aphasia use computers in their everyday life. The present study explored: (1) the use of computers by people with aphasia; and (2) the perceptions of people with aphasia towards computers and computer-based therapy. Thirty-four people with aphasia completed an aphasia-friendly paper-based survey about their use of computers before and after the onset of their aphasia, and their attitudes towards computer-based aphasia therapy. There was a high level of computer usage by people with aphasia both before and after the onset of their aphasia. However, the nature of the computer use changed following aphasia onset, with a move away from work-based usage. The majority of the cohort used computers for aphasia therapy and liked using computer-based aphasia therapy, provided that the programs were perceived as appropriate for their individual needs. The results highlight the importance of exposing people with aphasia to computer-based aphasia therapy in a supported clinical environment, and the need to ensure that computer-based therapy is individualised for each client. It should be noted, however, that while the majority of participants reported positive experiences with using computers, this does not mean that the computer-based therapy software used was necessarily an effective treatment for aphasia.

A Hostile World

2014 ◽  
pp. 232-253
Author(s):  
Maresa Bertolo ◽  
Ilaria Mariani
Keyword(s):  
Everyday Life ◽  
Urban Context ◽  
High Level ◽  
Persuasive Game

A Hostile World is a persuasive game designed for an urban context with a high level of multiethnic presence, a recurrent feature of the contemporary megalopolis. Our players are ordinary native citizens who are plunged into an alternative reality where they can realize how complex and demanding it is to deal with gestures and tasks of everyday life in a foreign context, trusting them to live a destabilizing experience that aims to increase the sensitivity, understanding, and empathy towards foreigners, soothing the existing multicultural tensions. The game is a quest-based system; quests recreate situations of everyday-life needs, from shopping to bureaucratic adventures; it's designed to be modular and its sessions may change in the number and quality of quests adapting to different cities, contexts, and targets. The authors identify its effectiveness through the analysis of data collected during and after actual gameplay.

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.

Evolutionary Algorithms

2016 ◽  
pp. 190-215 ◽  
Author(s):  
Ka-Chun Wong
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Real World ◽  
Fitness Function ◽  
Search Performance ◽  
Parallel Search ◽  
Survival Selection ◽  
Parent Selection ◽  
History Of ◽  
Search Capability

Inspired from nature, evolutionary algorithms have been proven effective and unique in different real world applications. Comparing to traditional algorithms, its parallel search capability and stochastic nature enable it to excel in search performance in a unique way. In this chapter, evolutionary algorithms are reviewed and discussed from concepts and designs to applications in bioinformatics. The history of evolutionary algorithms is first discussed at the beginning. An overview on the state-of-the-art evolutionary algorithm concepts is then provided. Following that, the related design and implementation details are discussed on different aspects: representation, parent selection, reproductive operators, survival selection, and fitness function. At the end of this chapter, real world evolutionary algorithm applications in bioinformatics are reviewed and discussed.

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