Openga, a C++ genetic algorithm library

In this paper, an open source C++ Genetic Algorithm library is proposed called openGA. This library is capable of optimization in each of single objective, multi-objective and interactive modes. The main motivation for proposing this library is to provide freedom to users for designing their custom solution data model without limitations which many currently available software/libraries suffer from such as forcing a user to define the solutions as vectors or limiting the output of evaluation functions to a predefined format. In addition, the user has the entire control over genetic operations such as solution creation, mutation and crossover. The multi-object mode performs a Non-dominated Sorting Genetic Algorithm known as NSGA-III to obtain the pareto-optimal front while preserving the solution diversity. This library can handle multi-threading computations for single and multi-objective problems to increase the speed of the calculations significantly. The interactive mode is suitable for applications where human subjectivity is involved for evaluation of the cost function. Several simulation and tests are performed to verify the effectiveness of this library for calculations of optimization problems.

[1]  Arthur Griffith.  GCC, the complete reference , 2002 .

[2]  Kalyanmoy Deb,et al.  An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints , 2014, IEEE Transactions on Evolutionary Computation.

[3]  Tomoyuki Hiroyasu,et al.  Discussion of Offspring Generation Method for Interactive Genetic Algorithms with Consideration of Multimodal Preference , 2008, SEAL.

[4]  Riccardo Poli,et al.  A Field Guide to Genetic Programming , 2008 .

[5]  Saeid Nahavandi,et al.  Defining sub-regions in locally sparsified compressive sensing MRI , 2013, BioMed 2013.

[6]  Matthew R. Lewis.  Evolutionary Visual Art and Design , 2008, The Art of Artificial Evolution.

[7]  Pedram Payvandy,et al.  Fashion set design with an emphasis on fabric composition using the interactive genetic algorithm , 2016, Fashion and Textiles.

[8]  Marco Laumanns,et al.  Scalable Test Problems for Evolutionary Multiobjective Optimization , 2005, Evolutionary Multiobjective Optimization.

[9]  Kenneth O. Stanley,et al.  A novel human-computer collaboration: combining novelty search with interactive evolution , 2014, GECCO.

[10]  R.SIVARAJ,et al.  A REVIEW OF SELECTION METHODS IN GENETIC ALGORITHM , 2011 .

[11]  Richard M. Stallman,et al.  Using the GNU Compiler Collection , 2010 .

[12]  Weng Kee Wong,et al.  Recent developments in optimal experimental designs for functional magnetic resonance imaging. , 2014, World journal of radiology.

[13]  Saeid Nahavandi,et al.  Adaptive Translational Cueing Motion Algorithm Using Fuzzy Based Tilt Coordination , 2014, ICONIP.

[14]  张焱,et al.  Improved Particle Filtering Techniques Based on Generalized Interactive Genetic Algorithm , 2017 .

[15]  Manjunatha Mahadevappa,et al.  Trajectory Path Planning of EEG Controlled Robotic Arm Using GA , 2015, IHCI.

[16]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[17]  Abdollah Homaifar,et al.  Identification of Switched Models in Non-Stationary Time Series based on Coordinate-Descent and Genetic Algorithm , 2015, GECCO.

[18]  Yuichiro Yamabe,et al.  Research on urban landscape design using the interactive genetic algorithm and 3D images , 2017 .

[19]  Saeid Nahavandi,et al.  Locally Sparsified Compressive Sensing in Magnetic Resonance Imaging , 2015 .

[20]  S. Momani,et al.  Solution of Inverse Kinematics Problem using Genetic Algorithms , 2016 .

[21]  Sung-Bae Cho,et al.  Emotional image and musical information retrieval with interactive genetic algorithm , 2004, Proc. IEEE.

[22]  Takeshi Hatanaka,et al.  Parallel distributed Interactive Genetic Algorithm for composing music melody suited to multiple users' feelings , 2016, 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS).

[23]  Saeid Nahavandi,et al.  A marginalised Markov Chain Monte Carlo approach for model based analysis of EEG data , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[24]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[25]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[26]  G. Yasuda,et al.  Sensor-based path planning and intelligent steering control of nonholonomic mobile robots , 2001, IECON'01. 27th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.37243).

[27]  Saeid Nahavandi,et al.  MPC-based motion cueing algorithm with short prediction horizon using exponential weighting , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[28]  Xin Yao,et al.  Parallel Problem Solving from Nature PPSN VI , 2000, Lecture Notes in Computer Science.

[29]  Manoj Kumar,et al.  Genetic Algorithm: Review and Application , 2010 .

[30]  Yee-Hong Yang,et al.  Multi-resolution stereo matching using genetic algorithm , 2001, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001).

[31]  Saeid Nahavandi,et al.  Future reference prediction in model predictive control based driving simulators , 2016, ICRA 2016.

[32]  Shigeru Nakayama,et al.  Using interactive evolutionary computation to generate creative building designs , 2008, Artificial Life and Robotics.

[33]  Saeid Nahavandi,et al.  Adaptive Washout Algorithm Based Fuzzy Tuning for Improving Human Perception , 2014, ICONIP.

[34]  Huosheng Hu,et al.  Robotic Dance in Social Robotics—A Taxonomy , 2015, IEEE Transactions on Human-Machine Systems.

[35]  Antonio Fernandes Dias,et al.  Multiobjective genetic algorithms applied to solve optimization problems , 2002 .

[36]  Saeid Nahavandi,et al.  Towards automated quality assessment measure for EEG signals , 2017, Neurocomputing.

[37]  Ana I. Pereira,et al.  An Optimization Approach for the Inverse Kinematics of a Highly Redundant Robot , 2015, AECIA.

[38]  Saeid Nahavandi,et al.  Non-uniform sparsity in rapid compressive sensing MRI , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[39]  Peter Sincak,et al.  Discovering Art in Robotic Motion: From Imitation to Innovation via Interactive Evolution , 2011, UCMA.

[40]  Arben Asllani,et al.  Using genetic algorithm for dynamic and multiple criteria web-site optimizations , 2007, Eur. J. Oper. Res..

[41]  Martin J. Oates,et al.  The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation , 2000, PPSN.

[42]  Kalyanmoy Deb,et al.  An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach , 2014, IEEE Transactions on Evolutionary Computation.

[43]  Hideyuki Takagi,et al.  Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation , 2001, Proc. IEEE.

[44]  Fumio Kojima,et al.  Multiple fuzzy state-value functions for human evaluation through interactive trajectory planning of a partner robot , 2006, Soft Comput..

[45]  Jim Smith,et al.  Evolving Atomic Aesthetics and Dynamics , 2016, EvoMUSART.

[46]  R. Saravanan,et al.  A multi-objective genetic algorithm (GA) approach for optimization of surface grinding operations , 2002 .

[47]  Nikolaus Hansen,et al.  Evaluating the CMA Evolution Strategy on Multimodal Test Functions , 2004, PPSN.

[48]  Kerim Çetinkaya,et al.  Comparison of four different heuristic optimization algorithms for the inverse kinematics solution of a real 4-DOF serial robot manipulator , 2015, Neural Computing and Applications.