Find Optimal Solution for Eggcrate Function Based on Objective Function
Languages of publication
In modern compilers and optimizers, the set of possible optimizations is usually very large. In general, for a given application and workload, optimization options do not accrue toward ultimate performance. To avoid selection complexity, users tend to use standard combination options, in general, however, these standard options are not the optimal set for a specific application executing its representative workload. Genetic algorithm developed by Goldberg was inspired by Darwin's theory of evolution which states that the survival of an organism is affected by rule "the strongest species that survives". Darwin also stated that the survival of an organism can be maintained through the process of reproduction, crossover and mutation. Darwin's concept of evolution is then adapted to computational algorithm to find solution to a problem called objective function in natural fashion. In this work test the function known as the “Eggcrate Function”; is described mathematically as: F(X) = X12 + X22 + 25(Sin2X1 + Sin2X2) In this problem, there are two design variables with lower and upper limits of [-2π , 2π]. We use genetic algorithm to find optimal solution for solving this problem, Basic philosophy of genetic algorithm and its flowchart are described. Step by step numerical computation of genetic algorithm for solving the eggcrate function will be briefly explained. The results shows that the eggcrate function has a known global minimum at [0, 0] with an optimal function value of zero.
-  Ahmed A. EL- Sawy, Mohamed A. Hussein, EL-Sayed M. Zaki, A. A. Mousa, ”An Introduction to Genetic Algorithms: A survey A practical Issues”, International Journal of Scientific & Engineering Research, Volume 5, Issue 1, January 2014.
-  Guy Bashkansky and Yaakov Yaari,” Black Box Approach for Selecting Optimization Options Using Budget-Limited Genetic Algorithms, 2007.
-  Ms. Dharmistha, D. Vishwakarma,” Genetic Algorithm based Weights Optimization of Artificial Neural Network”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 1, Issue 3, August 2012.
-  Larry Yaeger, ”Intro to Genetic Algorithms” Artificial Life as an approach to Artificial Intelligence” Professor of Informatics Indiana University, 2008.
-  Goldberg David E., “Genetic Algorithm in Search, Optimization, and Machine Learning”, Addison Wesley Longmont, International Student Edition 1989.
-  Andrey Popov, ”Genetic algorithms for optimization”, Hamburg, 2005.
-  Nanhao Zhu and Ian O’Connor,” iMASKO: A Genetic Algorithm Based Optimization Framework for Wireless Sensor Networks”, Journal of Sensor and Actuator Networks ISSN 2224-2708, www.mdpi.com/journal/jsan/, 2013.
-  Zhou Qing Qing and Purvis Martin, “A Market-Based Rule Learning System” aGuangDong Data Communication Bureau China Telecom 1 Dongyuanheng Rd., Yuexiunan, Guangzhou 510110, China, Department of Information Science, University of Otago, PO Box 56, Dunedin, New Zealand and/or improving the comprehensibility of the rules, 2004.
-  Abo Rajy, ”Genetic algorithms”, 2013.
-  Lubna Zaghlul Bashir, Rajaa Salih Mohammed, “Solving Banana (Rosenbrock) Function based on fitness function”, World Scientific News, 6 (2015) 41-56.
-  Bull Larry, “Learning Classifier Systems: A Brief Introduction”, Faculty of Computing, Engineering & Mathematical Sciences University of the West of England, Bristol BS16 1QY, U.K. Larry, 2004.
-  Olesya Peshko,”Global Optimization Genetic Algorithms”, 2007.
-  Kumara Sastry, David Goldberg, Graham Kendall, ”GENETIC ALGORITHMS”, University of Illinois, USA, University of Nottingham, UK, 2011.
-  Damian Schofield, Rong Qu,”Genetic algorithms”, 2012.
-  Mitchell Melanie,” An Introduction to Genetic Algorithms”, A Bradford Book the MIT Press Cambridge, Massachusetts - London, England, Fifth printing, 1999.
-  Saif Hasan, Sagar Chordia, Rahul Varshneya, ”Genetic algorithm”, February 6, 2012.
-  R.C. Chakraborty “Fandemantaels of genetic algorithms”: AI course lecture 39-40, notes, slides, 2010.
-  Andrew Chipperfield, Peter Fleming, Hartmut Pohlheim, Carlos Fonseca,” Genetic Algorithm TOOLBOX For Use with MATLAB”, Department of Automatic Control and System Engineering, 2001.
-  Lubna Zaghlul Bashir, ”Using Evolutionary Algorithm to Solve Amazing Problem by two ways: Coordinates and Locations”, World Scientific News 7 (2015) 66-87.
-  Lubna Zaghlul Bashir, ”Development of Adaptive Control Agent Simulation Using Genetic Algorithm”, A Thesis Submitted to the Department of Computer Science, University of Technology, 2005.
Publication order reference