2023 - Research.com Computer Science in Australia Leader Award
Ruhul A. Sarker mostly deals with Mathematical optimization, Evolutionary algorithm, Genetic algorithm, Optimization problem and Operations research. His Mathematical optimization study frequently draws parallels with other fields, such as Benchmark. His Evolutionary algorithm study integrates concerns from other disciplines, such as Population-based incremental learning, Cultural algorithm and Pareto principle.
His Genetic algorithm research includes themes of Algorithm and Job shop scheduling. Ruhul A. Sarker has researched Optimization problem in several fields, including Multi-objective optimization and Algorithm design. His Operations research research incorporates elements of Quality, Linear programming, Time horizon and Heuristic.
His primary areas of study are Mathematical optimization, Evolutionary algorithm, Optimization problem, Genetic algorithm and Evolutionary computation. His biological study spans a wide range of topics, including Job shop scheduling and Benchmark. His Evolutionary algorithm research is multidisciplinary, incorporating elements of Heuristic, Process and Cultural algorithm.
Ruhul A. Sarker interconnects Multi-objective optimization, Metaheuristic and Crossover in the investigation of issues within Optimization problem. His study focuses on the intersection of Genetic algorithm and fields such as Heuristic with connections in the field of Operations research. Ruhul A. Sarker combines subjects such as Range, Selection and Evolution strategy with his study of Differential evolution.
Ruhul A. Sarker mostly deals with Mathematical optimization, Optimization problem, Evolutionary algorithm, Differential evolution and Genetic algorithm. His study looks at the relationship between Mathematical optimization and fields such as Process, as well as how they intersect with chemical problems. His research in Optimization problem focuses on subjects like Microgrid, which are connected to Operating cost.
The study incorporates disciplines such as Evolutionary computation, Optimization algorithm, Algorithm design and Benchmark in addition to Evolutionary algorithm. The concepts of his Differential evolution study are interwoven with issues in Scheduling, Selection and Genetic operator. As part of one scientific family, he deals mainly with the area of Genetic algorithm, narrowing it down to issues related to the Segmentation, and often Network topology.
His scientific interests lie mostly in Mathematical optimization, Optimization problem, Evolutionary algorithm, Differential evolution and Evolutionary computation. Ruhul A. Sarker integrates Mathematical optimization and Profit in his studies. His research combines Genetic algorithm and Optimization problem.
He has researched Evolutionary algorithm in several fields, including Algorithm design and Computational intelligence. Ruhul A. Sarker interconnects Bidding, Selection and Genetic operator in the investigation of issues within Differential evolution. His Evolutionary computation research is multidisciplinary, incorporating perspectives in Pareto principle, Ecological selection and Benchmark.
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PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems
H.A. Abbass;R. Sarker;C. Newton.
congress on evolutionary computation (2001)
A Decomposition-Based Evolutionary Algorithm for Many Objective Optimization
M. Asafuddoula;Tapabrata Ray;Ruhul Sarker.
IEEE Transactions on Evolutionary Computation (2015)
Data Mining: A Heuristic Approach
Hussein Abbass;Charles Newton;Ruhul Sarker.
Differential Evolution With Dynamic Parameters Selection for Optimization Problems
Ruhul A. Sarker;Saber M. Elsayed;Tapabrata Ray.
IEEE Transactions on Evolutionary Computation (2014)
THE PARETO DIFFERENTIAL EVOLUTION ALGORITHM
Hussein A. Abbass;Ruhul A. Sarker.
International Journal on Artificial Intelligence Tools (2002)
Optimization of maintenance and spare provisioning policy using simulation
Ruhul Sarker;Amanul Haque.
Applied Mathematical Modelling (2000)
Ruhul Sarker;Xin Yao.
Multi-operator based evolutionary algorithms for solving constrained optimization problems
Saber M. Elsayed;Ruhul A. Sarker;Daryl L. Essam.
Computers & Operations Research (2011)
A new genetic algorithm for solving optimization problems
Saber M. Elsayed;Ruhul A. Sarker;Daryl L. Essam.
Engineering Applications of Artificial Intelligence (2014)
An improved evolutionary algorithm for solving multi-objective crop planning models
Ruhul Sarker;Tapabrata Ray.
Computers and Electronics in Agriculture (2009)
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