Budget optimization and allocation an evolutionary computing based model

Table of Contents Welcome Table of Contents Title BENTHAM SCIENCE PUBLISHERS LTD End User License Agreement (for non-institutional, personal use) Usage Rules: Disclaimer: Limitation of Liability: General: FOREWORD PREFACE DEDICATION SUMMARY Introduction Abstract 1.1 Importance and challenges of Budget Allocation in National and Global Economy Military Health Care Education 1.2 Advantages and disadvantages of Budget Allocation 1.2.1 Benefits of Budget Allocation 1.2.2 Disadvantage of Budget Allocation CONCLUDING REMARKS Consent for Publication CONFLICT OF INTEREST ACKNOWLEDGEMENTS REFERENCES Literature Review Abstract 2.1 Traditional Budget Allocation Technique 2.1.1 Rank and Selection Technique 2.1.2 Incremental Budgeting Technique 2.1.3 Zero-based Budgeting Technique 2.1.4 Ordinary Least Squares Technique (OLST) 2.1.5 Two-stage Least Squares Technique (2SLST) 2.2 Linear Optimization 2.3 Nonlinear Optimization 2.4 Metaheuristic Optimization 2.4.1 Pareto Efficiency or Pareto Optimality Marginal Conditions of Pareto Optimality Pareto Efficiency in Social welfare 2.4.2 Optimal Computing Budget Allocation (OCBA) 2.4.3 Genetic Algorithm (GA) 2.5 Literature Survey 2.6 Problem Statement CONCLUDING REMARKS Consent for Publication CONFLICT OF INTEREST ACKNOWLEDGEMENTS REFERENCES Research Methodology Abstract 3.1 Budget Allocation Scheme/Model 3.2 Budget Optimization Technique 3.2.1 Proposed Evolutionary Computing based Framework for Budget Allocation and Optimization 3.2.1.1 Mathematical Finance 3.2.1.1.1 Growth Rate 3.2.1.1.2 Percent Growth Rate 3.2.1.1.3 Mean, Variance and Standard Deviation 3.2.1.2 Evolutionary Computing Approach 3.2.1.2.1 Optimal Computing Budget Allocation (OCBA) 3.2.1.2.2 Genetic Algorithm Pseudo code for the Crossover Process 3.3 Budget Allocation Technique CONCLUDING REMARKS Consent for Publication CONFLICT OF INTEREST ACKNOWLEDGEMENTS REFERENCES Result and Discussion Abstract 4.1 TEST CASE 1: GROWTH RATE CALCULATION 4.2 TEST CASE 2: Percent Growth Rate 4.3 TEST CASE 3: Mean and Standard Deviation Technique 4.4 Optimization Technique 1: OCBA Technique 4.5 Optimization Technique 2: EA Technique 4.6 Optimization Technique 3: GA Optimization 4.7 Budget Allocation Technique 4.7.1 Scheme 1: National Council of Education Research and Training (NCERT) 4.7.2 Scheme 2: Kendriya Vidyalaya Sangathan (KVS) 4.7.3 Scheme 3: Central Tibetan School Society Administration 4.7.4 Scheme 4: Scheme for Setting Up 6000 Model Schools 4.7.5 Scheme 5: Rashtriya Madhyamik Shiksha Abhiyan (RMSA) 4.7.6 Scheme 6: Navodaya Vidyalaya Samiti (NVS) 4.8 Output of budget allocation CONCLUDING REMARKS Consent for Publication CONFLICT OF INTEREST ACKNOWLEDGEMENTS REFERENCES APPENDIX A Allocation OCBA Public Class Allocation_OCBA Applet Window Code for Simulation Simulation Pane Code for simulation OCBA and EA Simulation Run Optimal Computing Budget Allocation Simulation Equal Allocation (EA) Simulation Graph Generation APPENDIX B Simulation of Mean and Standard Deviation Calculation of Growth Rate Department Wise Budget Allocation Percentage Growth Rate Calculation APPENDIX C Budget Allocation Using Genetic Algorithm Approach Fitness Calculation GA Algorithm GA Population Selection Chart Preparation Java Bean for Mean Calculation Java Bean for Growth Rate Calculation LIST OF ABBREVIATIONS Budget Optimization and Allocation: An Evolutionary Computing Based Model Authored by Keshav Sinha Moumita Khowas Sidho-Kanho-Birsha University, Purulia West-Bengal, India Sudip Kumar Sahana Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India & Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India BENTHAM SCIENCE PUBLISHERS LTD End User License Agreement (for non-institutional, personal use) This is an agreement between you and Bentham Science Publishers Ltd Please read this License Agreement carefully before using the ebook/echapter/ejournal (“Work”) Your use of the Work constitutes your agreement to the terms and conditions set forth in 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automatically terminate without notice and without the need for a court order if at any point you breach any terms of this License Agreement In no event will any delay or failure by Bentham Science Publishers in enforcing your compliance with this License Agreement constitute a waiver of any of its rights You acknowledge that you have read this License Agreement, and agree to be bound by its terms and conditions To the extent that any other terms and conditions presented on any website of Bentham Science Publishers conflict with, or are inconsistent with, the terms and conditions set out in this License Agreement, you acknowledge that the terms and conditions set out in this License Agreement shall prevail Bentham Science Publishers Ltd Executive Suite Y - PO Box 7917, Saif Zone Sharjah, U.A.E Email: subscriptions@benthamscience.org FOREWORD The book titled “Budget Optimization and Allocation: An Evolutionary Computing Based Model” caters to a critical need in today’s intellectual landscape, viz., the problem of budget optimization and distribution and its solution The material covered in the book is an excellent balance of theory and practice The techniques discussed the attempt to synergise evolutionary computation (mainly genetic algorithm) with traditional approaches to budget allocation like optimal allocation, equal allocation, etc The attractiveness of the book comes from the fact that it takes as a case study the complex and vast problem of union budget of India The macro and micro issues discussed with attention to details, with the growth rate being the final aim of the budget exercise The second attractive aspect is that the authors compare and contrast the budget allocation practices of different countries, consistent with country’s economy, culture, population, etc The final attractiveness is the use of very modern methodologies like evolutionary computation to tackle incremental budgeting This book will be found useful by graduate students in their research I congratulate the authors on taking up a very timely and relevant problem Dr Pushpak Bhattacharyya Computer Science and Engineering, IIT Patna, static int getFitness(Individual individual) { int fitness = 0; for (int i = 100; i < individual.size() && i < solution.length; i++) { if (individual.getGene(i) == solution[i]) { fitness++; } } return fitness; } static int getMaxFitness() { int maxFitness = solution.length; return maxFitness; } } GA Algorithm public class Algorithm { private static final double uniformRate = 0.8; private static final double mutationRate = 0.02; private static final int tournamentSize = 5; private static final boolean elitism = true; public static Population evolvePopulation(Population pop) { Population newPopulation = new Population(pop.size(), false); if (elitism) { newPopulation.saveIndividual(0, pop.getFittest()); } int elitismOffset; if (elitism) { elitismOffset = 1; } else { elitismOffset = 0; } for (int i = elitismOffset; i < pop.size(); i++) { Individual indiv1 = tournamentSelection(pop); Individual indiv2 = tournamentSelection(pop); Individual newIndiv = crossover(indiv1, indiv2); newPopulation.saveIndividual(i, newIndiv); } for (int i = elitismOffset; i < newPopulation.size(); i++) { mutate(newPopulation.getIndividual(i)); } return newPopulation; } private static Individual crossover(Individual indiv1, Individual indiv2) { Individual newSol = new Individual(); for (int i = 0; i < indiv1.size(); i++) { if (Math.random()