Appraisal of genetic algorithm and its application in 0-1 knapsack problem

The Meta heuristic algorithms are the higher level technique which helps to find the best feasible solution out of all possible solution of an optimization problem. There are various different types of meta heuristic algorithms like Ant Colony Optimization (ACO), Genetic Algorithm, Simulated Annealing, Particle Swarm Optimization, etc. Genetic Algorithm is a search-based optimization technique based on the biological principle of Genetics and adaptation. It is a meta-heuristic approach which is used to solve complex combinatorial problem. The integration of Genetic algorithm with machine learning will be helpful in solving unconstrained and constrained optimization problem. The various genetic operator like selection operator, mutation and cross-over are discussed which will be helpful in knowing how these operators significantly improves State Space search. In this paper the various applications of Genetic algorithms which can be used in machine learning has been discussed. In this paper the author discussed how the significance of Genetic algorithm will be improved while solving complex optimization problem in machine learning. In this paper, flow diagram of Genetic Algorithms has been discussed which will ease the understanding of complex optimization problem like 0–1 Knapsack, Traveling Salesman Problem, etc. In this paper a comparative analysis between traditional algorithm and genetic algorithm has been done on the basis of parameters like flow of control, state space search, Complication, Preconditions, CPU utilization etc. The various limitations of Genetic Algorithms in solving problems with optimal solutions has also been discussed.

Application of genetic algorithm in designing high-voltage open-air substation lightning protection system

This study presents a comprehensive analysis of recent studies that explore the application of genetic algorithms (GAs) for optimizing solutions to the 0/1 Knapsack Problem (KP). The 0/1 Knapsack Problem, a classic combinatorial optimization challenge, involves selecting a subset of items with given weights and values to maximize the total value without exceeding a specified weight limit. Genetic algorithms, inspired by the principles of natural selection and genetics, have emerged as a powerful heuristic for tackling this NP-hard problem. Our review synthesizes findings from contemporary research, highlighting the effectiveness of various GA approaches, including standard GAs, hybrid models, and enhanced techniques incorporating local search and other optimization strategies. We evaluate the performance metrics, computational efficiency, and solution quality achieved by these methods. Additionally, we discuss the strengths and limitations of GAs in addressing the 0/1 Knapsack Problem, providing insights into their practical applications and potential improvements. The paper concludes with recommendations for future research directions, aiming to advance the state-of-the-art in genetic algorithm-based optimization for the 0/1 Knapsack Problem.

We develop and test a new approach for generating initial populations for the application of genetic algorithms (GA) to problems in combinatorial optimisation, specifically the multidimensional knapsack problem. We focus the empirical study of our approach on a set of two dimensional knapsack problems (2KP) used in a past study of 2KP algorithm performance. Our proposed approach for initial population generation focuses on generating populations that are stronger in terms of solution quality, solution diversity and in terms of solutions hovering near the border of feasible and infeasible solutions within the problem solution space. We report the results of a Monte Carlo experiment comparing our approach with the traditional initial population generation approach and report the results of computational tests involving 1120 2KP instances that cover a range of problem constraint characteristics. The collective of these computational results show that our proposed approach provides an initial population of sufficient quality and diversity to produce improved convergence to near optimal solutions which can equate to reduced computational burden in applications involving complex computations.

A genetic algorithm for online-scheduling of a multiproduct polymer batch plant

Genetic Algorithms (GAs) have been applied in many complex combinatorial optimization problems and have been proven to yield reasonably good solutions due to their ability of searching in continuous spaces and avoiding local optima. However, one issue in GA application that needs to be carefully explored is to examine sensitivity of critical parameters that may affect the quality of solutions. The key critical GA parameters affecting solution quality include the number of genetic operators, the number of encoded decision variables, the parameter for selective pressure, and the parameter for non-uniform mutation. The effect of these parameters on solution quality is particularly significant for complex problems of combinatorial nature. In this paper the authors test the sensitivity of critical GA parameters in optimizing 3-dimensional highway alignments which has been proven to be a complex combinatorial optimization problem for which an exact solution is not possible warranting the application of heuristics procedures, such as GAs. If GAs are applied properly, similar optimal solutions should be expected at each replication. The authors perform several example studies in order to arrive at a general set of conclusions regarding the sensitivity of critical GA parameters on solution quality. The first study shows that the optimal solutions obtained for a range of scenarios consisting of different combinations of the critical parameters are quite close. The second study shows that different optimal solutions are obtained when the number of encoded decision variables is changed.

0-1 knapsack problem is a typical optimization difficult problem. At present, there are a lot of methods to solve this problem, but with the increase of the scale of this problem, among them, the genetic algorithm is an important branch, it is quite active to ask the research of solving 0-1 knapsack on the basis of the genetic algorithm. This paper combines multi-agent theory and CGS-MSM PGA (coarse grain size-master slaver model parallel genetic algorithm) together, propose one improvement CGS-MSM PGA based on multi-agent, then use this PGA to solve 0-1 knapsack problem. This PGA is made up of many agent, solves the 0-1 knapsack problem by the coordination between many agents inside the multi-agent system. The introduction of multi-agent theory, make the master course and slave course of CGS-MSM PGA to be made of agent, so the ability of communication and coordination raise greatly, thus overcome the shortcoming of original CGS-MSM PGA; And comparing with other methods solved 0-1 knapsack problem, the method of this paper not only has the fast computational speed and the high precision, but also can get more optimal solving than other algorithms.

This is a research project on using Genetic Algorithm to solve 0-1 Knapsack Problem. Knapsack problem is a combinational optimization problem. Given a set of items, each with a weight & value, it determine the number of each item to include in a collection so that the total weight is less than a given limit & the total value is as large as possible. The paper consists of three parts. In the first section we give brief description of Genetic Algorithms and some of its basic elements. Next, we describe the Knapsack Problem and Implementation of Knapsack problem using Genetic Algorithm. The main purpose of this paper is to implement Knapsack problem by an algorithm that is based on Genetic Algorithm. In this paper I have used Roulette-Wheel, Tournament Selection and Stochastic selection as a selection function and the succeeding populations are analyzed for the fitness value with hope to achieve the correct solution and expected results were observed.

This paper considers genetic algorithm to process logistics path optimization. Genetic algorithms are significant in location issues, distribution issues, scheduling issues, transportation issues, and layout issues. A genetic algorithm for solving this problem is constructed base on the mathematical model of logistics distribution path optimization problem. The calculation results show that using genetic algorithm to optimize the logistics distribution path can easily and effectively obtain the optimal solution or approximate optimal solution.

The aim of this work is modeling the activities of 67 E. coli dihydrofolate reductase (DHFR) inhibitors 2,4-diamino-5-(substituted benzyl) pyramidines by optimization of correlation weights of local graph invariants (cwlgis), assisting genetic algorithm (GA). The relationship between the descriptors and molecular activities was modeled using radial basis function partial least squares (rbfPLS). Proper relation between activities and the optimized molecular descriptors were shown, R training = 0.92 and r test = 0.85. The results indicate promising potential for the optimization of a correlation weights scheme. It is the first application of GA in optimization of cwlgis. The study also shows the ability of rbfPLS, combined with GA, in a factor-based nonlinear QSAR modeling using simple flexible descriptors.

The knapsack problem is problem that is still often found in everyday life, one of which is the problem of selecting goods to be transported into containers for delivery of goods. This knapsack problem can be solved by using various optimization algorithms, one of which is the genetic algorithm. This study aims to design a genetic algorithm model to solve the 2-dimensional knapsack problem. 2-dimensional knapsack problem is a knapsack problem that has 2 constraints and in this study, the constraints used were weight and volume.. The evaluation results of the genetic algorithm will be compared with dynamic programming. From the evaluation results that have been carried out, it can be concluded that genetic algorithms can produce near-optimal results with faster computational times than dynamic programming.

This paper innovates, within the context of preventive maintenance planning, the application of Genetic Algorithm, as a modern powerful optimization tool, in minimum downtime strategies, for optimal part replacement. Therefore, a brief account of Genetic Algorithm is given and a computer program is written to aid the solution of such problems by Genetic Algorithm. Then, the program is used to solve a numerical example, concerning an equipment with Gaussian failure characteristics. The results obtained by Genetic Algorithm and the ordinary methods are compared to show that exactly the same results can be approached by the application of Genetic Algorithm. Genetic Algorithm and maintenance planning, both being subjects of statistical nature, can be combined to provide a vast area of interesting research work and, hence, some guidelines are suggested for further research.

In this study selection of optimum module, shaft diameter and rolling bearing for conical gear has been done using genetic algorithm (GA). GA, is a novel stochastic method of optimization. GAs are based on the principles of natural selection and evolutionary theory. Objective function was optimized for the design variables between determined boundary values. The GA was constrained by taking into account the power, moment, velocity, wall thickness and bearing distances. Tooth strength and surface crush were considered to be design constraints for module optimization. The other algorithm constraints are maximum bending and torsion moments for shaft optimization, and working life for bearing optimization.

Genetic algorithm is one of the optimization techniques, successfully applied in nuclear reactor environment for a variety of applications. Nuclear fuel management optimization is a classical nuclear engineering problem, which has been studied extensively and several techniques, including genetic algorithms, have been used for its solution. The study presented in this paper addresses the overall procedures and methods developed for the application of genetic algorithms in the optimization studies of nuclear fuel management. The result obtained from a study that explores a typical application of genetic algorithm in nuclear fuel management is also presented. Finding out of optimal number of fuel subassemblies in the core of a nuclear reactor (having power generating capacity of 500 MWe) is taken up for the study. The results demonstrate the suitability and efficiency of the algorithm in generating feasible solutions for the selected optimization problem.

Analysis of the human transfer impedance data (Ztr) with six-coefficient model requires using an optimisation algorithm minimising the square error criterion. Commonly used gradient algorithm do not lead to optimal solution in a single run, as the criterion function can have many local minima. So it is necessary to run the algorithm many times to be sure that the solution found is really optimal. We present an approach using single genetic algorithm applied (GA) to the analysis of Ztr measured in humans. The results from the analysis of Ztr data collected in 9 healthy volunteers in the range 2 to 64 Hz show that after a single run of GA we obtain a solution which is very close to optimal set of model parameters. As the solution found with GA is not optimal, it can be used as a starting point to classic method with gradient algorithm. We conclude, that the approach presented in the paper results in shortening of the time needed to calculate optimal parameters of the model and increases the certainty of finding optimal solution.