The optimal solution to minimizing the maximum tardiness in single machine scheduling is obtained by the Earliest Due Date (EDD) rule if ready times are zero for all jobs. In the case of non-zero ready times, preempti...
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The optimal solution to minimizing the maximum tardiness in single machine scheduling is obtained by the Earliest Due Date (EDD) rule if ready times are zero for all jobs. In the case of non-zero ready times, preemption becomes a significant consideration in providing a solution. Preemption allowed version is solved optimally by using the Modified Earliest Due Date (MEDD) procedure. However, the version of preemption not allowed is known as NP-hard and delay and non-delay strategies might be used in a hybrid fashion. This paper focuses on minimizing the maximum tardiness in the presence of non-zero times and when preemption is not allowed. The proposed method is evolutionary programming (EP). The results indicate that EP produces optimal / near optimal results consistently.
Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable *** constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been developed with th...
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Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable *** constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been developed with the use of different algorithmic strategies,evolutionary operators,and constraint-handling *** performance of CMOEAs may be heavily dependent on the operators used,however,it is usually difficult to select suitable operators for the problem at ***,improving operator selection is promising and necessary for *** work proposes an online operator selection framework assisted by Deep Reinforcement *** dynamics of the population,including convergence,diversity,and feasibility,are regarded as the state;the candidate operators are considered as actions;and the improvement of the population state is treated as the *** using a Q-network to learn a policy to estimate the Q-values of all actions,the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic *** framework is embedded into four popular CMOEAs and assessed on 42 benchmark *** experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.
Software effort estimation is a very difficult task carried out by software project managers as very little information is available in the early phases of software development. The information that we are collecting ...
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Software effort estimation is a very difficult task carried out by software project managers as very little information is available in the early phases of software development. The information that we are collecting about various attributes of software needs to be subjective which otherwise can lead to uncertainity. Inaccurate software effort estimation can be disastrous. Both underestimation and over estimation can lead to schedule overruns and incorrect estimation of budget for software development. Software effort estimation is a very crucial activity for project control, quality control and success of any software project. Software effort estimation fall under the categories of expert judgement, algorithmic and machine learning techniques. We have tried to analyse the performance of evolutionary techniques for software effort estimation. For this purpose various datasets with different properties have been collected. After that various evolutionary algorithms like FRSBM-R, GFS-SAP-Sym-R, GFS-GAP-Sym-R, NNEP-R, GANN-R, GFS-GP-R, GFS-GSP-R, GFS- RB-MF-R, CART-R, Linear_LMS-R, NU_SVR-R, EPSILON_SVR-R etc have been used. Performance is measured in terms of various accuracy measures like MMRE, MRE, PRED(25), PRED(50) and PRED(75). Results of our research have shown that evolutionary algorithms give more accurate results for software effort estimation as compared to traditional methods of software effort estimation. Moreover the comparison of different evolutionary algorithms is done to find which evolutionary learning algorithm is better for which situation.
By remarkably reducing real fitness evaluations, surrogate-assisted evolutionary algorithms (SAEAs) have been successfully applied to expensive optimization problems. However, existing SAEAs generally ignore the wides...
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Now-a-days, it is important to find out solutions of Multi-Objective Optimization Problems (MOPs). evolutionary Strategy helps to solve such real world problems efficiently and quickly. But sequential evolutionary Alg...
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The philosophy of evolutionary algorithms is to emulate nature in selecting individuals in a population who will populate future generations. In order to speed up the evolutionary process in selecting the best individ...
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ISBN:
(纸本)9781618395528
The philosophy of evolutionary algorithms is to emulate nature in selecting individuals in a population who will populate future generations. In order to speed up the evolutionary process in selecting the best individuals, a fitness function is usually constructed to evaluate the current goodness of individuals in a population, based upon past and current information. However, such a function should not only evaluate the current goodness of individuals but should also predict (perceive) the future goodness of individuals. Further, tuning the mutation rate and crossover rate to produce new elitism by such a fitness function should improve the convergence of generations to a set of best individuals. This is the premise taken here and a modified evolutionary algorithm is developed by designing a heuristic fitness function that incorporates prediction. Simulation results are provided to compare the approach to traditional fitness function strategies.
Differential evolution （DE） is a kind of evolutionary algorithms, which is suitable for solving complex optimization problems. Mutation is a crucial step in DE that generates new solutions from old ones. It was argu...
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Differential evolution （DE） is a kind of evolutionary algorithms, which is suitable for solving complex optimization problems. Mutation is a crucial step in DE that generates new solutions from old ones. It was argued and has been commonly adopted in DE that the solutions selected for mutation should have mutually different indices. This restrained condition, however, has not been verified either theoretically or empirically yet. In this paper, we empirically investigate the selection of solutions for mutation in DE. From the observation of the extensive experiments, we suggest that the restrained condition could be relaxed for some classical DE versions as well as some advanced DE variants. Moreover, relaxing the restrained condition may also be useful in designing better future DE algorithms.
Complexity is commonly summarized as‘the actions of the whole are more than the sum of the actions of the parts’.Understanding how the coherence emerges from these natural and artificial systems provides a radical s...
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Complexity is commonly summarized as‘the actions of the whole are more than the sum of the actions of the parts’.Understanding how the coherence emerges from these natural and artificial systems provides a radical shift in the process of thought,and brings huge promises for controlling and fostering this *** authors define the term‘Complex System Engineering’to denote this approach,which aims at transferring the radical insights from Complex System Science to the pragmatic world of engineering,especially in the Computing System Engineering domain.A theoretical framework for Complex System Engineering is built by the morphogenetic engineering framework,which identifies a graduation of models,in growing order of generative *** implementation of Complex System Engineering requires a portfolio of operational solutions:The authors therefore provide a classification of Complex System application approaches to answer this challenge and support the emergence of Complex System Engineers capable of addressing the issues of an ever more connected world.
Conformer searching algorithms find minima in the Potential Energy Surface (PES) of a molecule, usually by following a torsion-driven approach. The minima represent conformers, which are interchangeable via free rotat...
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Conformer searching algorithms find minima in the Potential Energy Surface (PES) of a molecule, usually by following a torsion-driven approach. The minima represent conformers, which are interchangeable via free rotation around bonds. Conformers can be used as input to computational analyses, such as drug design, that can convey molecular reactivity, structure, and function. With an increasing number of rotatable bonds, finding optima in the PES becomes more complicated, as the dimensionality explodes. Kaplan is a new, free and open-source software package written by the author that uses a ring-based evolutionary Algorithm (EA) to find conformers. The ring, which contains population members (or pmems), is designed to allow initial PES exploration, followed by exploitation of individual energy wells, such that the most energetically-favourable structures are returned. The strengths and weaknesses of existing publicly available conformer searchers are discussed, including Balloon, RDKit, Openbabel, Confab, Frog2, and Kaplan. Since RDKit is usually considered to be the best free package for conformer searching, its conformers for the amino acids were optimised using the MMFF94 forcefield and compared to the conformers generated by Kaplan. Amino acid conformers are well characterised, and provide insight for protein substructure. Of the 20 molecules, Kaplan found a lower energy minima for 12 of the structures and tied for 5 of them. Kaplan allows the user to specify which dihedrals (by atom indices) to optimise and angles to use, a feature that is not offered by other programs. The results from Kaplan were compared to a known dataset of amino acid conformers. Kaplan identified all 57 conformers of methionine to within 1.2A, and found identical conformers for the 5 lowest-energy structures (i. e. within 0.083A), following forcefield optimisation.
During the past decade,research efforts have been gradually directed to the widely existing yet less noticed multimodal multi-objective optimization problems(MMOPs)in the multi-objective optimization ***,researchers h...
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During the past decade,research efforts have been gradually directed to the widely existing yet less noticed multimodal multi-objective optimization problems(MMOPs)in the multi-objective optimization ***,researchers have begun to investigate enhancing the decision space diversity and preserving valuable dominated solutions to overcome the shortage caused by a preference for objective space ***,many existing methods still have limitations,such as giving unduly high priorities to convergence and insufficient ability to enhance decision space *** overcome these shortcomings,this article aims to explore a promising region(PR)and enhance the decision space diversity for handling *** traditional methods,we propose the use of non-dominated solutions to determine a limited region in the PR in the decision space,where the Pareto sets(PSs)are included,and explore this region to assist in solving ***,we develop a novel neighbor distance measure that is more suitable for the complex geometry of PSs in the decision space than the crowding *** on the above methods,we propose a novel dual-population-based coevolutionary *** studies on three benchmark test suites demonstrates that our proposed methods can achieve promising performance and versatility on different *** effectiveness of the proposed neighbor distance has also been justified through comparisons with crowding distance methods.
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