Genetic Algorithm Solution Research Articles

A novel hybrid feature selection via Symmetrical Uncertainty ranking based local memetic search algorithm

A novel correlation based memetic framework (MA-C) which is a combination of genetic algorithm (GA) and local search (LS) using correlation based filter ranking is proposed in this paper. The local filter method used here fine-tunes the population of GA solutions by adding or deleting features based on Symmetrical Uncertainty (SU) measure. The focus here is on filter methods that are able to assess the goodness or ranking of the individual features. Empirical study of MA-C on several commonly used datasets from the large-scale Gene expression datasets indicates that it outperforms recent existing methods in the literature in terms of classification accuracy, selected feature size and efficiency. Further, we also investigate the balance between local and genetic search to maximize the search quality and efficiency of MA-C.

Change in optimum genetic algorithm solution with changing band discontinuities and band widths of electrically conducting copolymers

The interest in copolymers stems from the fact that they present interesting electronic and optical properties leading to a variety of technological applications. In order to get a suitable copolymer for a specific application, genetic algorithm (GA) along with negative factor counting (NFC) method has recently been used. In this paper, we study the effect of change in the ratio of conduction band discontinuity to valence band discontinuity (Δ E c/Δ E v) on the optimum solution obtained from GA for model binary copolymers. The effect of varying bandwidths on the optimum GA solution is also investigated. The obtained results show that the optimum solution changes with varying parameters like band discontinuity and band width of constituent homopolymers. As the ratio Δ E c/Δ E v increases, band gap of optimum solution decreases. With increasing band widths of constituent homopolymers, the optimum solution tends to be dependent on the component with higher band gap.

Comparison of stochastic methods with respect to performance and reliability of low‐temperature gas separation processes

AbstractIn this paper, the performances of two popular stochastic methods, the genetic algorithm (GA) and simulated annealing (SA), are verified in the optimization of low‐temperature gas separation processes. While the feasibility of GA optimization of low‐temperature processes has recently been addressed, our work studied the quality of GA solutions. Having optimized the solutions of three different case studies, it was observed that SA is more robust and reliable than GA when applied to such systems, and by adjusting the key parameters in the SA method, the optimization process can avoid pre‐mature convergence and is able to give the best near‐global results.

Comprehensive bioeconomic modelling of multiple harmful non-indigenous species

Harmful non-indigenous species (NIS) introductions lead to loss of biodiversity and serious economic impacts. Government agencies have to decide on the allocation of limited resources to manage the risk posed by multiple NIS. Bioeconomic modelling has focused on single species and little is known about the optimal management of multiple NIS using a common budget. A comprehensive bioeconomic model that considers the exclusion, detection and control of multiple NIS spreading by stratified dispersal and presenting Allee effects was developed and applied to manage the simultaneous risk posed by Colorado beetle, the bacterium causing potato ring rot and western corn rootworm in the UK. A genetic algorithm was used to study the optimal management under uncertainty. Optimal control methods were used to interpret and verify the genetic algorithm solutions. The results show that government agencies should allocate less exclusion and more control resources to NIS characterised by Allee effects, low rate of satellite colonies generation and that present low propagule pressure. The prioritisation of NIS representative of potential NIS assemblages increases management efficiency. The adoption of management measures based on the risk analysis of a single NIS might not correspond to the optimal allocation of resources when other NIS share a common limited budget. Comprehensive bioeconomic modelling of multiple NIS where Allee effects and stratified dispersal are considered leads to a more cost-effective allocation of limited resources for the management of NIS invasions.

Multiobjective genetic algorithm solution to the optimum economic and environmental performance problem of small autonomous hybrid power systems with renewables

The overall evaluation of small autonomous hybrid power systems (SAHPS) that contain renewable and conventional power sources depends on economic and environmental criteria, which are often conflicting objectives. The solution of this problem belongs to the field of non-linear combinatorial multiobjective optimisation. In a multiobjective optimisation problem, the target is not to find an optimal solution, but a set of non-dominated solutions called Pareto-set. The present article considers as an economic objective the minimisation of system's cost of energy (COE), whereas the environmental objective is the minimisation of the total greenhouse gas (GHG) emissions of the system during its lifetime. The main novelty of this article is that the calculation of GHG emissions is based on life cycle analysis (LCA) of each system's component. In LCA, the whole life cycle emissions of a component are taken into account, from raw materials extraction to final disposal/recycling. This article adopts the non-dominated sorting genetic algorithm (NSGA-II), which in combination with a proposed local search procedure effectively solves the multiobjective optimisation problem of SAHPS. Two main categories of SAHPS are examined with different energy storage: lead-acid batteries and hydrogen storage. The results indicate the superiority of batteries under both economic and environmental criteria.

A new dispatching rule based genetic algorithm for the multi-objective job shop problem

Hyper-heuristics or "methodologies to choose heuristics" are becoming increasingly popular given their suitability to solve hard real world combinatorial optimisation problems. Their distinguishing feature is that they operate in the space of heuristics or heuristic components rather than in the solution space. In Dispatching Rule Based Genetic Algorithms (DRGA) solutions are represented as sequences of dispatching rules which are called one at a time and used to sequence a number of operations onto machines. The number of operations that each dispatching rule in the sequence handles is a parameter to which DRGA is notoriously sensitive. This paper proposes a new hybrid DRGA which searches simultaneously for the best sequence of dispatching rules and the number of operations to be handled by each dispatching rule. The investigated DRGA uses the selection mechanism of NSGA-II when handling multi-objective problems. The proposed representation was used to solve different variants of the multi-objective job shop problem as well as the single objective problem with the sum of weighted tardiness objective. Our results, supported by the statistical analysis, confirm that DRGAs that use the proposed representation obtained better results in both the single and multi-objective environment overall and on each particular set of instances than DRGAs using the conventional dispatching rule representation and a GA that uses the more common permutation representation.

Genetic Algorithm Solution of Network Coding Optimization

After the best optimizing approach of network coding is being studied, some methods are proposedbased on the characteristics of the network coding overhead optimization problem. First, two modifications areadded to the preprocessing phase: 1) How to generate a fitness value to a network coding scheme under a certainnetwork coding optimization request is presented. This makes different network coding optimization problems besolved with the same genetic algorithm. 2) An additional exam processing of the multi-in outgoing links is importedto reduce the solution space. Second, experimental results show that the random generated solution of networkcoding optimization problem can hardly achieve the multicast rate, three new steps are suggested be taken with thecommon genetic algorithm: 1) use more delicate member generating function to generate initial members; 2) addnew members at the beginning of each round of the genetic algorithm to avoid localized optimization; 3) assign afitness value based on each receiver’s data rate rather than ?1 to those network coding solutions which cannoachieve the max multicast rate. Experimental results show dramatic improvements in terms of both efficiency andresult.

Elite guided steady-state genetic algorithm for minimizing total tardiness in flowshops

In this research, a detailed study of the permutation flowshop scheduling problem with the objective of minimizing total tardiness is presented and a steady-state genetic algorithm solution procedure is developed for such problems. Also, using problem-specific knowledge, a very efficient elite guided solution improvement scheme and an appropriate crossover operator have been developed and integrated into the proposed method. Using benchmark problems, the algorithm has been compared with heuristic algorithms having the best performance in the literature. The performance of the developed algorithm is shown to be superior using a simulation study.

A genetic algorithm for maximum-weighted tree matching problem

In hierarchal organizations, for assigning tasks to the divisions of the organization some constraints must be satisfied. This article investigates one such problem in which there are k different tasks to be accomplished and each division’s performance on each task may be different and represented by a scalar value. In this article we formally introduce this real life decision problem, named as Maximum-Weighted Tree Matching Problem, and propose a genetic algorithm solution to it, and give some experimental results.

GENETIC ALGORITHM MODEL FOR PROFIT MAXIMIZATION OF GENERATING COMPANIES IN DEREGULATED ELECTRICITY MARKETS

In deregulated and rapidly changing electricity markets, there is strong interest on how to solve the new price-based unit commitment (PBUC) problem used by each generating company to optimize its generation schedule in order to maximize its profit. This article proposes a genetic algorithm (GA) solution to the PBUC problem. The advantages of the proposed GA are: 1) flexibility in modeling problem constraints because the PBUC problem is not decomposed either by time or by unit; 2) smooth and easier convergence to the optimum solution thanks to the proposed variable fitness function which not only penalizes solutions that violate the constraints but also this penalization is smoothly increasing as the number of generations increases; 3) easy implementation to work on parallel computers, and 4) production of multiple unit commitment schedules, some of which may be well suited to situations that may arise quickly due to unexpected contingencies. The method has been applied to systems of up to 120 units and the results show that the proposed GA constantly outperforms the Lagrangian relaxation PBUC method for systems with more than 60 units. Moreover, the difference between the worst and the best GA solution is very small, ranging from 0.10% to 0.49%.

S1401-1-5 作業者の技能と教育を考慮した動的作業計画 : 作業配分ルールの最適化(生産システムの新展開(基礎・理論))

This study deals with a simulation-based scheduling method to dispatch jobs to workers in consideration of learning by On-the-Job Training (OJT). As of skilliul work such as maintenance work of trains and airplanes, workers have to learn many jobs with OJT. OJT is important to keep skill level and long-term work efficiency of the organization. In order to schedule in consideration of OJT, two important requirements must be satisfied One is to keep the deadline, and another is to keep at least one skilled worker for every job. In previous study, we proposed a method for controlling frequency of OJT to adjust the balance of these two criteria. In this paper, an optimization method by using Genetic Algorithm solution. Case study shows that the proposed method generates better solutions than the previous one.

Induction over Strategic Agents: a genetic algorithm solution

We study the problem called Induction over Strategic Agents. This problem has proven hard to solve, even for small problems. We start by reducing the problem to an unconstrained search over w∈ℜ n . Once we accomplish this, we develop a Genetic Algorithm to perform this search. We compare our results to those obtained on a Mixed Integer formulation.

Genetic Algorithm Approach for Solving the Task Assignment Problem

In this paper a genetic algorithm (GA) for the task assignment problem (TAP) is considered.An integer representation with standard genetic operators is used. Computational results are presented for instances from the literature, and compared to optimal solutions obtained by the CPLEX solver. It can be seen that the proposed GA approach reaches 17 of 20 optimal solutions. The GA solutions are obtained in a quite a short amount of computational time.

Multilevel Redundancy Allocation Optimization Using Hierarchical Genetic Algorithm

This paper proposes a generalized formulation for multilevel redundancy allocation problems that can handle redundancies for each unit in a hierarchical reliability system, with structures containing multiple layers of subsystems and components. Multilevel redundancy allocation is an especially powerful approach for improving the system reliability of such hierarchical configurations, and system optimization problems that take advantage of this approach are termed multilevel redundancy allocation optimization problems (MRAOP). Despite the growing interest in MRAOP, a survey of the literature indicates that most redundancy allocation schemes are mainly confined to a single level, and few problem-specific MRAOP have been proposed or solved. The design variables in MRAOP are hierarchically structured. This paper proposes a new variable coding method in which these hierarchical design variables are represented by two types of hierarchical genotype, termed ordinal node, and terminal node. These genotypes preserve the logical linkage among the hierarchical variables, and allow every possible combination of redundancy during the optimization process. Furthermore, this paper developed a hierarchical genetic algorithm (HGA) that uses special genetic operators to handle the hierarchical genotype representation of hierarchical design variables. For comparison, the customized HGA, and a conventional genetic algorithm (GA) in which design variables are coded in vector forms, are applied to solve MRAOP for series systems having two different configurations. The solutions obtained when using HGA are shown to be superior to the conventional GA solutions, indicating that the HGA here is especially suitable for solving MRAOP for series systems.

Genetic algorithm for collaboration cost optimization-oriented partner selection in virtual enterprises

Partner selection is a key step to organize a well-designed virtual enterprise in which collaboration cost between partners is considered to be one of the most important performance indicators that influence the run-time efficiency of virtual enterprise. However, in current solutions there is usually a lack of precise and comprehensive considerations on the collaborations, which makes the selection results not optimal enough. The paper elaborately analyses various collaboration patterns between distributed partners with the corresponding evaluation metrics for collaboration time and cost. A genetic algorithm solution for collaboration cost optimization-oriented partner selection is then developed. The performance of this algorithm is demonstrated by a case, and the results of this trial demonstrate the real life capability of the algorithm.

Optimal design method for building energy systems using genetic algorithms

In this paper, a new optimal design method for building energy systems is proposed. This method provides the most efficient energy system, best combination of equipment capacity and best operational planning for cooling, heating, and power simultaneously with respect to certain criteria such as energy consumption, CO 2 emission, etc. Specifically for this paper, the authors apply this method to a sample building as a case study. The “Genetic Algorithms (GA)” optimization method, which can resolve nonlinear optimization problems, is adopted for this optimization analysis. Also its applicability is analyzed in a case study. In order to validate the accuracy of this method, the correct optimum solution based on comprehensive inquiries is also calculated. A comparison of the GA solution with the correct solution demonstrates fairly good agreement. The results show that the proposed method is sufficiently capable of determining the optimal design and has the potential to be applied to very complex energy systems with appropriate modifications.

Computing minimal doubly resolving sets of graphs

In this paper we consider the minimal doubly resolving sets problem (MDRSP) of graphs. We prove that the problem is NP-hard and give its integer linear programming formulation. The problem is solved by a genetic algorithm (GA) that uses binary encoding and standard genetic operators adapted to the problem. Experimental results include three sets of ORLIB test instances: crew scheduling, pseudo-boolean and graph coloring. GA is also tested on theoretically challenging large-scale instances of hypercubes and Hamming graphs. Optimality of GA solutions on smaller size instances has been verified by total enumeration. For several larger instances optimality follows from the existing theoretical results. The GA results for MDRSP of hypercubes are used by a dynamic programming approach to obtain upper bounds for the metric dimension of large hypercubes up to 2 90 nodes, that cannot be directly handled by the computer.

Structural optimisation of 3D damage tolerant components comparing the biological and genetic algorithm solutions

This paper studies the 3D optimal design of two generic aircraft components and a rail component. Experimental studies of fatigue crack growth in aluminium alloys have shown that, at the low-to-mid stress intensity factor range; there is often a log-linear relationship between the crack length and the fatigue life. These observations have led to the development of the ‘generalised Frost–Dugdale’ crack growth law, which allowed the accurate prediction of fatigue crack growth from the low-to-mid stress intensity factor range. For this research paper the ‘generalised Frost–Dugdale’ law was used to perform the optimisation study of 7050-T7451 aluminium based structures. The optimal solutions produced by the biological and genetic algorithms are compared. The results indicated that the optimal geometrical shape and predicted fatigue life depended on the crack sizes and structural geometry. A comparison study between the solutions generated by the biological and genetic algorithms indicated that both algorithms produced similar solutions. As a result, this paper illustrates a procedure, based on the damage tolerance philosophy, for the design of light weight structures using fatigue based optimisation in conjunction with a genetic algorithm.

A simulation/optimization model for the identification of unknown groundwater well locations and pumping rates

In this study, a simulation/optimization (S/O) model is proposed for the identification of unknown groundwater well locations and pumping rates for two-dimensional aquifer systems. The proposed S/O model uses a finite-difference solution of governing groundwater flow equation as simulation model. This model is then combined with a genetic algorithm (GA) based optimization model which is used to determine the pumping rates for each well. To determine the well locations, an iterative moving sub-domain approach is proposed. The main advantage of this approach is that the optimization model only determines the pumping rates and it does not require the well locations as decision variables. The well locations are implicitly determined based on the results of pumping rate optimizations for different pre-defined well locations within sub-domains. The performance of the proposed S/O model is tested on two hypothetical aquifer models for both steady-state and transient flow conditions. In both cases, the identification procedure starts with one pumping well and systematically increases the number of the pumping wells until the best well configuration is identified. Determination of the best number of pumping wells is performed based on the residual errors (RE) between simulated and observed piezometric head values for given observation sites. Results indicated that when the number of pumping wells is greater than the true number of wells, the identified well configuration approaches to the true well configuration. Moreover, under steady-state flow conditions, the robustness of the proposed moving sub-domain approach is tested for different initial locations of sub-domains. Results showed that the true well locations are identified wherever the search process starts. Finally, the performance of the proposed S/O model is compared with a pure GA solution in which the well locations and pumping rates are treated as decision variables. Results indicate that the proposed S/O model finds smaller RE than the pure GA solution by performing 14% less simulations.

Combination of Genetic Algorithm and Inverse Solution Technique for Estimating the Hydraulic Properties of Unsaturated Soft Rock

Unsaturated hydraulic property (relation between the water retention curve and hydraulic conductivity function) of soft rock must be properly estimated for analyzing the saturated-unsaturated groundwater flow such as the flow around a tunnel, rock surface and so forth. In this study a practical technique for estimating the unsaturated hydraulic property of soft rock was developed on the basis of the Campbell model and Genetic Algorithm (GA). The major objectives of the present study are the following two; 1) investigation whether or not the Campbell model can be applied to soft rock for approximating the unsaturated hydraulic properties and 2) application of GA technique for estimating two parameters included in the Campbell model. These parameters were estimated through the inverse analysis of the transient change of evaporation rates from a surface of disk shaped specimen of Tertiary sedimentary rock which are sampled from the Rokkasho area, Aomori prefecture. Two transient evaporation experiments under different atmospheric conditions were proposed for the estimation. The Campbell's parameters of 13 soft rock specimens (5 sandstone and 8 tuff breccia specimens) were estimated through those types of experiments. As the results it was clearly found that the transient change of evaporation rates can be well fitted by the Campbell model except for the region of low saturation and this fact implies that the Campbell model can be applied to the soft rock. And also it was found that, the GA is a promising technique for parameters estimation in the inverse solution method. It can be concluded that the techniques are practically for estimating the unsaturated hydraulic properties of soft rock.