A Survey: Genetic Algorithms and the Fast Evolving World of Parallel Computing

Munawar, Asim; Wahib, Mohamed; Munetomo, Masaharu; Akama, Kiyoshi

doi:10.1109/hpcc.2008.77

Cited by 26 publications

(14 citation statements)

References 8 publications

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“…Many variants of GAs exists depending on evaluation method of new chromosomes, a calculation method using serial or parallel processors, combination with some local optimization algorithms (hill climbing, etc), and other factors [23].…”

Section: Optimization Methods Using Pso and Gamentioning

confidence: 99%

Comparison between RLS-GA and RLS-PSO for Li-ion battery SOC and SOH estimation: a simulation study

Rozaqi

Rijanto

Kanarachos

2017

J. Mechatron. Electr. Power Veh. Technol.

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This paper proposes a new method of concurrent SOC and SOH estimation using a combination of recursive least square (RLS) algorithm and particle swarm optimization (PSO). The RLS algorithm is equipped with multiple fixed forgetting factors (MFFF) which are optimized by PSO. The performance of the hybrid RLS-PSO is compared with the similar RLS which is optimized by single objective genetic algorithms (SOGA) as well as multi-objectives genetic algorithm (MOGA). Open circuit voltage (OCV) is treated as a parameter to be estimated at the same time with internal resistance. Urban Dynamometer Driving Schedule (UDDS) is used as the input data. Simulation results show that the hybrid RLS-PSO algorithm provides little better performance than the hybrid RLS-SOGA algorithm in terms of mean square error (MSE) and a number of iteration. On the other hand, MOGA provides Pareto front containing optimum solutions where a specific solution can be selected to have OCV MSE performance as good as PSO.

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Section: Optimization Methods Using Pso and Gamentioning

confidence: 99%

Comparison between RLS-GA and RLS-PSO for Li-ion battery SOC and SOH estimation: a simulation study

Rozaqi

Rijanto

Kanarachos

2017

J. Mechatron. Electr. Power Veh. Technol.

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“…If the random number r i and q 10 <r<q 11 , v i =v 11 . Obviously, some chromosomes would be selected more than once.…”

Section: Choicementioning

confidence: 99%

“…They are a particular class of Evolutionary Algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover. GAs constructs a population of probable solutions and evolves it over the generations to find a better individual that is close to the optimal solution [11]. GAs are a part of evolutionary computing, which is a rapidly growing area of artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

Design of Genetic Algorithm-based Parking System for an Autonomous Vehicle

Xiong

Choi

2009

International Journal of Fuzzy Logic and Intelligent Systems

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A Genetic Algorithm (GA) is a kind of search techniques used to find exact or approximate solutions to optimization and searching problems. This paper discusses the design of a genetic algorithm-based intelligent parking system. This is a search strategy based on the model of evolution to solve the problem of parking systems. A genetic algorithm for an optimal solution is used to find a series of optimal angles of the moving vehicle at a parking space autonomously. This algorithm makes the planning simpler and the movement more effective. At last we present some simulation results.

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“…The large literature on distributed and parallel genetic algorithms dates back to Grosso [11], who explored the idea of subdividing a GA population into smaller subpopulations with occasional exchanges of fit individuals among the populations. More recent work ranges from implementations tailored to particular hardware configurations [12,22] to a wide variety of algorithms in which the population is partitioned and individuals are shared among the partitions according to different schemes, e.g., [22]. Parameter tuning (population size, migration rate, etc.)…”

Section: Related Workmentioning

confidence: 99%

Automated repair of binary and assembly programs for cooperating embedded devices

et al. 2013

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We present a method for automatically repairing arbitrary software defects in embedded systems, which have limited memory, disk and CPU capacities, but exist in great numbers. We extend evolutionary computation (EC) algorithms that search for valid repairs at the source code level to assembly and ELF format binaries, compensating for limited system resources with several algorithmic innovations. Our method does not require access to the source code or build toolchain of the software under repair, does not require program instrumentation, specialized execution environments, or virtual machines, or prior knowledge of the bug type.We repair defects in ARM and x86 assembly as well as ELF binaries, observing decreases of 86% in memory and 95% in disk requirements, with 62% decrease in repair time, compared to similar source-level techniques. These advances allow repairs previously possible only with C source code to be applied to any ARM or x86 assembly or ELF executable. Efficiency gains are achieved by introducing stochastic fault localization, with much lower overhead than comparable deterministic methods, and low-level program representations.When distributed over multiple devices, our algorithm finds repairs faster than predicted by naïve parallelism. Four devices using our approach are five times more efficient than a single device because of our collaboration model. The algorithm is implemented on Nokia N900 smartphones, with inter-phone communication fitting in 900 bytes sent in 7 SMS text messages per device per repair on average.

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A Survey: Genetic Algorithms and the Fast Evolving World of Parallel Computing

Cited by 26 publications

References 8 publications

Comparison between RLS-GA and RLS-PSO for Li-ion battery SOC and SOH estimation: a simulation study

Comparison between RLS-GA and RLS-PSO for Li-ion battery SOC and SOH estimation: a simulation study

Design of Genetic Algorithm-based Parking System for an Autonomous Vehicle

Automated repair of binary and assembly programs for cooperating embedded devices

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