Evolutionary Algorithm with Roulette-Tournament Selection for Solving Aquaculture Diet Formulation

Rahman, Rosshairy Abd; Ramli, Razamin; Jamari, Zainoddin; Ku-Mahamud, Ku Ruhana

doi:10.1155/2016/3672758

Cited by 30 publications

(24 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this case, tournament selection is the process of evaluating and comparing the fitness of various individuals within a population. In binary tournament selection, two individuals are chosen at random, the fitnesses are evaluated, and the individual with the better solution is selected [1].…”

Section: Main Loopmentioning

confidence: 99%

Optimizing carbon tax for decentralized electricity markets using an agent-based model

Kell

McGough

Forshaw

2020

Proceedings of the Eleventh ACM International Conference on Future Energy Systems

View full text Add to dashboard Cite

Averting the effects of anthropogenic climate change requires a transition from fossil fuels to low-carbon technology. A way to achieve this is to decarbonize the electricity grid. However, further efforts must be made in other fields such as transport and heating for full decarbonization. This would reduce carbon emissions due to electricity generation, and also help to decarbonize other sources such as automotive and heating by enabling a low-carbon alternative. Carbon taxes have been shown to be an efficient way to aid in this transition.In this paper, we demonstrate how to to find optimal carbon tax policies through a genetic algorithm approach, using the electricity market agent-based model ElecSim. To achieve this, we use the NSGA-II genetic algorithm to minimize average electricity price and relative carbon intensity of the electricity mix. We demonstrate that it is possible to find a range of carbon taxes to suit differing objectives.Our results show that we are able to minimize electricity cost to below £10/MWh as well as carbon intensity to zero in every case. In terms of the optimal carbon tax strategy, we found that an increasing strategy between 2020 and 2035 was preferable. Each of the Pareto-front optimal tax strategies are at least above £81/tCO2 for every year. The mean carbon tax strategy was £240/tCO2. CCS CONCEPTS• Computing methodologies → Agent / discrete models; Genetic algorithms.

show abstract

Section: Main Loopmentioning

confidence: 99%

Optimizing carbon tax for decentralized electricity markets using an agent-based model

Kell

McGough

Forshaw

2020

Proceedings of the Eleventh ACM International Conference on Future Energy Systems

View full text Add to dashboard Cite

show abstract

“…A number of individuals are selected at random from the population for later breeding and the fittest individual among these selected individuals is chosen as a parent. This technique is selected because it is (i) coding efficient [24] (ii) has the capability to manage either maximisation or minimisation problems without performing any structural changes [25], and (iii) has the ability to create more diverse populations by providing a uniform probability for all population individuals to be in the new generation [23], [26].…”

Section: B the Search Algorithmmentioning

confidence: 99%

A Metaheuristic Search Framework to Derive Cancer Care Services from Business Process Models

Aljawawdeh

Odeh

Simons

et al. 2018

2018 1st International Conference on Cancer Care Informatics (CCI)

View full text Add to dashboard Cite

Cancer Care involves not only handling patients' medical or physical needs but also other services to facilitate patient needs which are underpinned by appropriate software systems that assist in patient care processes. The Service-Oriented Architecture (SOA) model of computing has become widely adopted and can provide efficient and agile business solutions in the face of rapid changes to business requirements. Instead of adopting a more traditional way of building an IT system for Cancer Care by rigidly piecing together a collection of hardware, software and networking, SOA offers the opportunity to build the IT systems in an increasingly flexible and reconfigurable way. However, current service identification methods can suffer from shortcomings such as a lack of computational support, and not being able to address all the necessary activities of the service identification. To address these shortcomings, this paper presents a comprehensive metaheuristic search framework for deriving SOA-based services applied to Cancer Care business process models. This framework is evaluated using both quantitative and qualitative methods with the help of domain experts at King Hussein Cancer Centre (KHCC), Jordan. Evaluation by domain experts confirmed that the resulting services are feasible (i.e., valid services that can be practically applied for real-life projects) that the domain experts might not have arrived at manually. Statistical analysis shows candidate services produced by the search-based framework are superior to the services produced manually by domain experts at KHCC with respect to metrics for coupling and cohesion.

show abstract

“…Another popular study to adjust the probabilistic noise level throughout the mating pool to regulate the selection pressure [7]. Abd-Rahman et al [8] established a hybrid roulette-tournament selection operator for solving a realvalued shrimp diet formulation problem which can also be generalized to evolutionary algorithm-related problems. A detailed study is about the selection process in GA and examined some common issues in various selection operators in Ref.…”

Section: Introductionmentioning

confidence: 99%

Trade-off between exploration and exploitation with genetic algorithm using a novel selection operator

Hussain

Shad

2019

Complex Intell. Syst.

View full text Add to dashboard Cite

As an intelligent search optimization technique, genetic algorithm (GA) is an important approach for non-deterministic polynomial (NP-hard) and complex nature optimization problems. GA has some internal weakness such as premature convergence and low computation efficiency, etc. Improving the performance of GA is a vital topic for complex nature optimization problems. The selection operator is a crucial strategy in GA, because it has a vital role in exploring the new areas of the search space and converges the algorithm, as well. The fitness proportional selection scheme has essence exploitation and the linear rank selection is influenced by exploration. In this article, we proposed a new selection scheme which is the optimal combination of exploration and exploitation. This eliminates the fitness scaling issue and adjusts the selection pressure throughout the selection phase. The χ 2 goodness-of-fit test is used to measure the average accuracy, i.e., mean difference between the actual and expected number of offspring. A comparison of the performance of the proposed scheme along with some conventional selection procedures was made using TSPLIB instances. The application of this new operator gives much more effective results regarding the average and standard deviation values. In addition, a two-tailed t test is established and its values showed the significantly improved performance by the proposed scheme. Thus, the new operator is suitable and comparable to established selection for the problems related to traveling salesman problem using GA.

show abstract

Evolutionary Algorithm with Roulette-Tournament Selection for Solving Aquaculture Diet Formulation

Cited by 30 publications

References 14 publications

Optimizing carbon tax for decentralized electricity markets using an agent-based model

Optimizing carbon tax for decentralized electricity markets using an agent-based model

A Metaheuristic Search Framework to Derive Cancer Care Services from Business Process Models

Trade-off between exploration and exploitation with genetic algorithm using a novel selection operator

Contact Info

Product

Resources

About