Crossover Method Research Articles

Dual relaxations of the time-indexed ILP formulation for min–sum scheduling problems

Linear programming (LP)-based relaxations have proven to be useful in enumerative solution procedures for \(\mathbf {NP}\)-hard min–sum scheduling problems. We take a dual viewpoint of the time-indexed integer linear programming (ILP) formulation for these problems. Previously proposed Lagrangian relaxation methods and a time decomposition method are interpreted and synthesized under this view. Our new results aim to find optimal or near-optimal dual solutions to the LP relaxation of the time-indexed formulation, as recent advancements made in solving this ILP problem indicate the utility of dual information. Specifically, we develop a procedure to compute optimal dual solutions using the solution information from Dantzig–Wolfe decomposition and column generation methods, whose solutions are generally nonbasic. As a byproduct, we also obtain, in some sense, a crossover method that produces optimal basic primal solutions. Furthermore, the dual view naturally leads us to propose a new polynomial-sized relaxation that is applicable to both integer and real-valued problems. The obtained dual solutions are incorporated in branch-and-bound for solving the total weighted tardiness scheduling problem, and their efficacy is evaluated and compared through computational experiments involving test problems from OR-Library.

Integrated scheduling algorithm based on the priority constraint table for complex products with tree structure

Aiming at the existing intelligent optimization algorithms for solving the integrated scheduling problem of complex products with tree structure, there are problems of missing optimal solutions when designing encoding methods or generating infeasible offspring while designing evolutionary operators, an integrated scheduling algorithm based on the priority constraint table is proposed in this paper. A novel encoding method based on the dynamic priority constraint table is developed, which can guarantee the feasibility and completeness of the initial population individuals. For the legitimacy of the generated offspring individuals, two new different crossover and mutation methods are designed separately. The introduced evolutionary operators can avoid the detection and repairment of the infeasible individuals. An insertion-based greedy decoding method is also developed. In addition, based on the critical operations, a local search strategy is presented to enhance the search ability for the superior solutions. The feasibility and superiority of the proposed algorithm is verified by comparative experiments.

EvoMCTS: A Scalable Approach for General Game Learning

In this paper, we present the application of genetic programming as a generic game learning approach to zero-sum, deterministic, full-knowledge board games by evolving board-state evaluation functions to be used in conjunction with Monte Carlo tree search (MCTS). Our method involves evolving board-evaluation functions that are then used to guide the MCTS playout strategy. We examine several variants of Reversi, Dodgem, and Hex using strongly typed genetic programming, explicitly defined introns, and a selective directional crossover method. Our results show a proficiency that surpasses that of baseline handcrafted players using equal and in some cases greater amounts of search, with little domain knowledge and no expert domain knowledge. Moreover, our results exhibit scalability.

Process of 3D wireless decentralized sensor deployment using parsing crossover scheme

A Wireless Sensor Networks (WSN) usually consists of numerous wireless devices deployed in a region of interest, each able to collect and process environmental information and communicate with neighboring devices. It can thus be regarded as a Multi-Agent System for territorial security, where individual agents cooperate with each other to avoid duplication of effort and to exploit other agent’s capacities. The problem of sensor deployment becomes non-trivial when we consider environmental factors, such as terrain elevations. Due to the fact that all sensors are homogeneous, the chromosomes that encode sensor positions are actually interchangeable, and conventional crossover schemes such as uniform crossover would cause some redundancy as well as over-concentration in certain specific geographical area. We propose a Parsing Crossover Scheme that intends to reduce redundancy and ease geographical concentration pattern in an effort to facilitate the search. The proposed parsing crossover method demonstrates better performances than those of uniform crossover under different terrain irregularities.

Applied Genetic Algorithm for Solving Rich VRP

In this article, we present a study of the effectiveness of a genetic algorithm (GA) to solve a combinatorial problem, that is, a vehicle routing problem (VRP). We propose a new selection method, called “rank and select,” based on selection rate, and we compare it with roulette wheel selection. In this article, we use two types of crossover method and two types of mutation method. These are applied for comparing the best fitness at the end of a generation. The problem solved in this study is how to generate feasible route combinations for a rich VRP and meet all the requirements with an optimum solution. Initial test results show that the route produced by the GA was effectively used for solving rich VRP and especially for a large number of customers, depots, and vehicles. Fuel consumption by proposed routes was lower by about 20.38% compared to that of an existing route.

PRM192 - Adjusting for Treatment Crossover in A Trametinib Metastatic Melanoma Rct: Identifying the Appropriate Method

Treatment crossover refers to the situation in randomised controlled trials (RCTs) where patients randomised to the control group switch to the experimental treatment. This leads to biased estimates of treatment effects if crossover is not appropriately controlled for. Several crossover adjustment methods are available, but previous research has shown that the optimal adjustment method depends upon the characteristics of the trial [1]. This study applies crossover adjustment methods to an RCT comparing trametinib to chemotherapy in patients with BRAFV600E/K mutation-positive advanced or metastatic melanoma (NCT01245062), and investigates which adjustment method best fits this case study. Patients enrolled in the METRIC clinical trial were randomised 2:1 to receive trametinib 2 mg once daily or chemotherapy (DTIC or paclitaxel). There were 273 patients in the primary efficacy population (trametinib, n = 178, chemotherapy, n = 95) and 64 (67.4%) chemotherapy control group patients switched onto the experimental treatment.

The Extended Crossover Peng–Robinson Equation of State for Describing the Thermodynamic Properties of Pure Fluids

In this paper, a theoretical method has been introduced for developing the crossover Peng–Robinson (CPR) equation of state (EoS) which incorporates the non-classical scaling laws asymptotically near the critical point into a classical analytic equation further away from the critical point. The CPR EoS has been adopted to describe the thermodynamic properties of some pure fluids (normal alkanes from methane to n-butane and carbon dioxide) such as density, saturated pressure, isochoric heat capacity and speed of sound. Unlike the original method for the crossover transformation made by Chen et al. (Phys Rev A 42:4470–4484, 1990), we have proposed a procedure which adding an additional term into the crossover transformation to obtain the thermophysical properties at the critical point more exactly. It is shown that this new crossover method yields a satisfactory representation of the thermodynamic properties close to the critical point for pure fluids relative to the original PR EoS.

Crossover Equation of State for Selected Hydrocarbons (C4–C7)

The organic Rankine cycle (ORC) has attracted attention for waste heat recovery and renewable energy systems. An accurate prediction for thermodynamic properties of working fluids is of great importance for cycle performance evaluations and system design. Particularly, hydrocarbons are promising for their good performance and low global warming potentials. Moreover, the thermal efficiency of the ORC is higher when the evaporation temperature is closer to the critical temperature, which makes the properties in the critical region rather important. Recent research has shown that using mixture as working fluid can achieve better temperature matches. Therefore, an equation of state (EoS) that can be extended to mixture calculations is more attractive. Specific EoS for selected hydrocarbons is precise, but very complex. Cubic EoSs, such as widely used Peng–Robinson EoS and Soave–Redlich–Kwong (SRK) EoS, fail to accurately predict liquid densities over wide pressure ranges or pressure–density–temperature (pρT) properties in the near-critical region. This work combines the volume translation approach and the crossover method to provide better prediction for thermodynamic properties in the critical region and in regions far from the critical point. A crossover volume translation SRK EoS is developed and used for n-butane, i-butane, n-pentane, i-pentane, n-hexane, i-hexane and n-heptane. The volume translation term is set as a constant to ensure the accuracy of the saturated liquid density at low reduced temperatures. Then, the crossover method is introduced into the volume translation EoS to improve the predictions of thermodynamic properties in the critical region. Six crossover parameters are used, which are constants or functions of acentric factor and critical parameters. Therefore, none of the parameters in the crossover volume translation SRK EoS is adjustable, which makes the crossover EoS totally predictive and easily extend to mixtures. Comparisons show that the crossover EoS is in much better agreement with experimental data than the original SRK EoS.

Genetic algorithm with maximum-minimum crossover (GA-MMC) applied in optimization of radiation pattern control of phased-array radars for rocket tracking systems.

In launching operations, Rocket Tracking Systems (RTS) process the trajectory data obtained by radar sensors. In order to improve functionality and maintenance, radars can be upgraded by replacing antennas with parabolic reflectors (PRs) with phased arrays (PAs). These arrays enable the electronic control of the radiation pattern by adjusting the signal supplied to each radiating element. However, in projects of phased array radars (PARs), the modeling of the problem is subject to various combinations of excitation signals producing a complex optimization problem. In this case, it is possible to calculate the problem solutions with optimization methods such as genetic algorithms (GAs). For this, the Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) method was developed to control the radiation pattern of PAs. The GA-MMC uses a reconfigurable algorithm with multiple objectives, differentiated coding and a new crossover genetic operator. This operator has a different approach from the conventional one, because it performs the crossover of the fittest individuals with the least fit individuals in order to enhance the genetic diversity. Thus, GA-MMC was successful in more than 90% of the tests for each application, increased the fitness of the final population by more than 20% and reduced the premature convergence.

Wireless sensor network design through genetic algorithm

In this paper, we used WSN design, as a multi-objective optimization problem through Genetic Algorithm (GA) technique. We examined the effects of GA parameters including population size, mutation probability, and selection and crossover methods on the design. Choosing suitable parameters is a trade-off between different network criteria and characteristics. Type of deployment, effect of network size, radio communication radius, deployment density of sensors in an application area and location of base station are the WSN’s characteristics, which were investigated in this paper. The simulation results of this study indicate that the value of radio communication radius has direct effect on radio interference, cluster-overlapping, sensor node distribution uniformity and communication energy consumption. The optimal value of radio communication radius depends on network deployment density rather than network size and deployment type. Location of the base station affects radio communication energy, cluster-overlapping and average number of communication per cluster head. BS located outside the application domain is preferred over that located inside. In all the network situations, random deployment has better performance compared to grid deployment.

IMPACT OF GENETIC ALGORITHM OPERATORS ON ITS PERFORMANCE IN SOLVING FLOW SHOP SCHEDULING PROBL

The primary objective of flow shop scheduling is to obtain the best sequence which optimizes various objectives such as makespan, total flow time, total tardiness, or number of tardy jobs, etc. Due to the combinatorial nature of the flow shop problem (FSP) there is a lot of artificial intelligence methods proposed to solve it. The Genetic Algorithm (GA), one of these methods, is considered a valuable search algorithm capable of finding a reasonable solution in a short computational time. GA operators, (selection, crossover and mutation process), give different forms that can be combined to give various GAs. In this paper we investigate the impact of selection, crossover and mutation process on the quality of the GA solution in solving the flow shop scheduling problems. In this study, four selection methods, seventeen crossover methods and eight mutation methods are investigated. The computational results show that there are significant differences among the investigated methods on the performance of the proposed GA.

Shared Crossover Method for Solving Knapsack Problems

A surprising number of everyday problems are difficult to solve by traditional algorithm. A problem may qualify as difficult for a number of different reasons; for example, the data may be too noisy or irregular, the problem may be difficult to model; or it may simply take too long to solve. It’s easy to find examples: finding the shortest path connecting a set of cities, dividing a set of different tasks among a group of people to meet a deadline, or fitting a set of various sized boxes into the fewest trucks. In the past, programmers might have carefully hand crafted a special purpose program for each problem; now they can reduce their time significantly using a Genetic Algorithm (GAs). A Genetic Algorithm is key to solve knapsack problem, the goal of this paper is to show that successful Genetic Algorithm for solving and implementation knapsack problem, Genetic Algorithms are stochastic whose search methods model some natural phenomena. Genetic algorithms are relatively easy for finding the optimal solution, or approximately optimum value of NP-Complete problems, the coding scheme I’ve chosen for the knapsack uses a fixed-length, binary, position-dependent string, from the result, I find that crossover and mutation operation control exploration while the selection and fitness function control exploitation. Mutation increases the ability to explore new areas of the search space but it also disrupts the exploitation of the previous generation by changing them.

SCHEDULING AND POWER DISTRIBUTION SYSTEM ON DIESEL POWER PLANT IN NORTH SULAWESI USING GENETIC ALGORITHM

Research regarding scheduling and power distribution system on diesel power plant in North Sulawesi using genetic algorithm has been done. This research was conducted to provide a new approach for scheduling and power distribution in North Sulawesi. Method of genetic algorithm is used in this study to address the problem of scheduling and distribution system of diesel power plant. Method of genetic algorithms is a heuristic search algorithm that mimics the mechanisms of biological system. Under this concept, the problem will be solved using the pattern of biological evolution. Solutions for solving problems will apply the method of selection, crossover, and mutation. Scheduling using genetic algorithm can optimize the scheduling done by by PT. PLN North Sulawesi, Minahasa System AP2B. Based on the results and the discussion it can be concluded that the genetic algorithm can be used as an efficient method for scheduling and power distribution system on the diesel power plants in North Sulawesi.

DOA and Power Estimation Using Genetic Algorithm and Fuzzy Discrete Particle Swarm Optimization

Aiming to reduce the computational costs and converge to global optimum, a novel method is proposed to solve the optimization of a cost function in the estimation of direction of arrival (DOA). In this method, genetic algorithm (GA) and fuzzy discrete particle swarm optimization (FDPSO) are applied to optimize the direction of arrival and power parameters of the mode simultaneously. Firstly, the GA algorithm is applied to make the solution fall into the global searching. Secondly, the FDPSO method is utilized to narrow down the search field. In FDPSO, chaotic factor and crossover method are added to speed up the convergence. This approach has been demonstrated through some computational simulations. It is shown that the proposed algorithm can estimate both the DOA and the powers accurately. It is more efficient than some present methods, such as Newton-like algorithm, Akaike information critical (AIC), particle swarm optimization (PSO), and genetic algorithm with particle swarm optimization (GA-PSO).

Study on Different Crossover Mechanisms of Genetic Algorithm for Test Interval Optimization for Nuclear Power Plants

Surveillance tests are performed periodically on standby systems of a Nuclear Power Plant (NPP), as they improve the systems' availability on demand. High availability of safety critical systems is very essential to NPP safety, hence, careful analysis is required to schedule the surveillance activities for such systems in a cost effective way without compromising the plant safety. This forms an optimization problem wherein, two different cases can be formulated for deciding the value of Surveillance Test Interval. In one case, cost is the objective function to be minimized while unavailability is constrained to be at a given level and in another case, unavailability is minimized for a given cost level. Here, optimization is done using Genetic Algorithm (GA) and real encoding has been employed as it caters well to the requirements of this problem. A detailed procedure for GA formulation is described in this paper. Two different crossover methods, arithmetical crossover and blend crossover are explored and compared in this study to arrive at the most suitable crossover method for such type of problems.

A Method of the Green Product Configuration Design Based on Multi-Layer Generalized Operator and Genetic Algorithm

Taking the configuration design of the lifting device in a bridge crane as an example, this paper discusses the green optimal design method of enterprise's modular products with uncertain factors. The multi-layer generalized operators and comprehensive mathematical model under uncertainty are established based on the mapping relationship between product function and its structure. The model is transformed to an ascertaining combinatorial optimization model by de-fuzzy operator, and then it is solved by GA (Genetic Algorithm, GA). The method of chromosome encoding in binary that the chromosome is segmented by components and the genes in each segment are ranged as corresponding structures of each component, and the methods of selection and crossover and mutation operator are presented in this paper. The result of the green configuration design on the lifting device verifies the effectiveness and practical value of the method proposed in this paper.

A Method of Optimal Module Partition in Green Product Configuration Design under Uncertainty

The principle and main steps of module partition in green product configuration design under uncertainty, and how to determine the correlation between the basic elements of a product, and how to calculate the green degree of module are discussed in this paper. Let the maximum degree of cluster inner a module and the minimum coupling degree among modules and the maximum green degree of modules as the objective function, the mathematic model of uncertainly optimizing for green module partition is set up. And then It is transformed to an ascertainable combinatorial optimization model by de-fuzzy operator, and it is solved by GA (Genetic Algorithm,). The methods of chromosome encoding and the methods of selection and crossover and mutation operator are presented in this paper. A computational example is studied; its result verifies the effectiveness and practical value of the method proposed in this paper.

Research on the Design of the Rock Drift's Rapid Excavation Construction Organization on the East Wing of Xinsan Mine

The east wing of the third new mine of Fengfeng Group co., LTD has been designed to construct about 2490m with a long service time, bad geological conditions and slowly driving speed. Through improving rock drivage construction techniques, the mine has taken measures of driving by three teams, a scientific construction organization of digging while spraying and a kind of advancing crossover operation method of after four in the first four. In addition, the mine also strengthens the management and takes scientific and reasonable scoring model and acceptance criteria. Through all these measures the mine has improved the driving speed effectively and make on the single lane rock drivage footage of more than 120 m good grades. The effect of roadway supporting and forming is of good quality and it has never had safety accidents. Whats more, the economic benefits and social benefits are obvious. It provides an important reference significance for similar quick rock drivage project.

On the interpretation of seismic cone penetration test (SCPT) results

Abstract The paper deals with the methodology of performing and interpretation of seismic cone penetration test (SCPT). This type of test is used to determine velocity of the seismic wave in the soil medium. This study is focused on shear wave. The wave is triggered on the ground surface by hitting an anvil with a sledgehammer. Then, vibrations induced at different depths are measured. Based on recorded measurements wave velocity (Vs) and thus also small strain shear modulus Gmax may be calculated. An interpretation of exemplary seismic test results is presented. Crossover and cross-correlation methods are discussed and another, more adequate one is featured and then applied in the interpretation example. Conditions for correct test performance and interpretation are discussed.

A novel hybrid artificial bee colony algorithm with crossover operator for numerical optimization

Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm intelligence algorithms which inspired by the foraging behavior of honey bee swarms. It has been widely used in numerical and engineering optimization problems. This paper presents a hybrid artificial bee colony (HABC) model to improve the canonical ABC algorithm. The main idea of HABC is to enhance the information exchange between bees by introducing the crossover operator of genetic algorithm to ABC. With suitable crossover operation, valuable information is fully utilized and it is expected that the algorithm can converge faster and more accurate. Eight versions of HABC algorithm combined by different selection and crossover methods under the model were proposed and tested on several benchmark functions. Then, the settings of the new parameter crossover rate for two well performed HABC versions are tested to verify their best values. Finally, four rotated functions and four shifted functions are used to test the performance of the two algorithms on complex functions and asymmetric functions. Experiment results showed that these two versions of HABC algorithm offer significant improvement over the original ABC and are superior to other two state of the art algorithms on some functions.