Genetic Algorithm With Species And Sexual Selection

Raghuwanshi, M. M.; Kakde, O. G.

doi:10.1109/iccis.2006.252229

Cited by 19 publications

(13 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We use two of the most popular genetic operators: real-coded simulated binary crossover [16,25] and polynomial mutation [16,26]. In each genetic operation, one of the two operators is chosen randomly but conforms to a predefined ratio.…”

Section: B Genetic Operationsmentioning

confidence: 99%

Machine based optimization using genetic algorithms in a storage ring

Tian

Safranek

Yan

2014

Phys. Rev. ST Accel. Beams

View full text Add to dashboard Cite

The genetic algorithm (GA) has been a popular technique in optimizing the design of particle accelerators. As a population based algorithm, GA requires a large number of evaluations of the objective functions, which can be time consuming. One can benefit from parallel computing with significantly reduced computing time when fulfilling the function evaluation by a numerical machine model in simulation codes. Indeed, this is the most common approach in GA applications. In this paper, instead of applying GA in the conventional numerical calculations as described above, we present a successful experimental demonstration of implementing GA in real machine based optimization. We conduct the minimization of the average vertical beam size of the SPEAR3 storage ring using GA. Beam loss rate is chosen as the sole objective function because it is inversely proportional to the vertical beam size and can be measured instantaneously in SPEAR3. The decision variables are the strengths of SPEAR3 skew quadrupoles, by varying which we can change both the betatron coupling and the vertical dispersion while searching for the minimum beam size. The results in this paper can shed light on new applications of GAs in the particle accelerator community, for example, optimizing the luminosity of a high energy collider or the injection efficiency of a diffraction limited storage ring in real time.

show abstract

Section: B Genetic Operationsmentioning

confidence: 99%

Machine based optimization using genetic algorithms in a storage ring

Tian

Safranek

Yan

2014

Phys. Rev. ST Accel. Beams

View full text Add to dashboard Cite

show abstract

“…Less common is the idea to divide the individuals into multiple populations and allow them to mate according to "gender" [49]. Biological concepts such as fertility and maturity are adapted by [50], [51]. The technique proposed by Zhu et al in [52] highlights the use of gender in multipopulation GA in an effort to reduce the danger of premature convergence.…”

Section: Multipopulation Gamentioning

confidence: 99%

“…Another version of the male-female duality was proposed by Raghuwanshi and Kakde [51]. In their system, FAS3, females are treated as "niches" (center points) of subspecies in the population.…”

Section: Multipopulation Gamentioning

confidence: 99%

Instinct-Based Mating in Genetic Algorithms Applied to the Tuning of 1-NN Classifiers

Quirino¹,

Kubát²,

Bryan³

2010

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

The behavior of the genetic algorithm (GA), a popular approach to search and optimization problems, is known to depend, among other factors, on the fitness function formula, the recombination operator, and the mutation operator. What has received less attention is the impact of the mating strategy that selects the chromosomes to be paired for recombination. Existing GA implementations mostly choose them probabilistically, according to their fitness function values, but we show that more sophisticated mating strategies can not only accelerate the search, but perhaps even improve the quality of the GA-generated solution. In our implementation, we took inspiration from the "opposites-attract" principle that is so common in nature. As a testbed, we chose the problem of 1-NN classifier tuning where genetic solutions have been employed before, and are thus well-understood by the research community. We propose three "instinct-based" mating strategies and experimentally investigate their behaviors.

show abstract

“…It has three major operators -Mutation, Crossover and Selection. These operators have been extended by different researchers for their defined targets, like parent-centric crossover operator (PCCO) [16] was extended as MPX or MLX operator. Genetic Algorithm is used in different problem solving scenarios like Job Shop Scheduling [17], Cellular Automata Urban Growth Model [18], Double Inverted Pendulum [19] and Hardware Implementation (Altera FPGA chip with 8051 processor chip) [20].…”

Section: B Genetics In Computer Sciencementioning

confidence: 99%

Defining Marriage and Birth operators based on Community Sticker for Community Algorithm

Siddiqui

Shaikh

Memon

2010

2010 International Conference on Information and Emerging Technologies

View full text Add to dashboard Cite

Communities are being established by human beings based on some common characteristics. Marriage and Birth operators are defined in order to address different Human Communities, based on Community Sticker. This Community Sticker and operators will be used in our on-going research of Development of Community Algorithm. The Community Sticker will be linked with Community Ontology and Human Community Ontology, which defines a way to hold different communities and especially human communities in them. These operators will use defined chromosomes of human in a community. It will process two individuals in an existing generation and producing a new individual in the upcoming generation of a human community.

show abstract

Genetic Algorithm With Species And Sexual Selection

Cited by 19 publications

References 6 publications

Machine based optimization using genetic algorithms in a storage ring

Machine based optimization using genetic algorithms in a storage ring

Instinct-Based Mating in Genetic Algorithms Applied to the Tuning of 1-NN Classifiers

Defining Marriage and Birth operators based on Community Sticker for Community Algorithm

Contact Info

Product

Resources

About