Customized crossover in evolutionary sets of safe ship trajectories

The paper presents selected aspects of evolutionary sets of safe ship trajectories-a method which applies evolutionary algorithms and some of the assumptions of game theory to solving ship encounter situations. For given positions and motion parameters of the ships, the method finds a near optimal set of safe trajectories of all ships involved in an encounter. The method works in real time and the solutions must be returned within one minute, which enforces speeding up the optimisation process. During the development of the method the authors tested various problem-dedicated crossover operators to obtain the best performance. The results of that research are given here. The paper includes a detailed description of these operators as well as statistical simulation results and examples of experiment results

[1]  Subbarao Kambhampati,et al.  Evolutionary Computing , 1997, Lecture Notes in Computer Science.

[2]  Rafal Szlapczynski,et al.  Evolutionary Sets of Safe Ship Trajectories: Problem Dedicated Operators , 2011, ICCCI.

[3]  Ngoc Thanh Nguyen,et al.  Computational Collective Intelligence. Technologies and Applications , 2014, Lecture Notes in Computer Science.

[4]  Hans-Paul Schwefel,et al.  How to analyse evolutionary algorithms , 2002, Theor. Comput. Sci..

[5]  Zbigniew Michalewicz,et al.  Modeling of ship trajectory in collision situations by an evolutionary algorithm , 2000, IEEE Trans. Evol. Comput..

[6]  Francisco Luna,et al.  Advances in parallel heterogeneous genetic algorithms for continuous optimization , 2004 .

[7]  Wojciech Jaskowski,et al.  Evolving small-board Go players using coevolutionary temporal difference learning with archives , 2011, Int. J. Appl. Math. Comput. Sci..

[8]  Janusz Mrozek,et al.  A neural-network controlled dynamic evolutionary scheme for global molecular geometry optimization , 2011, Int. J. Appl. Math. Comput. Sci..

[9]  Pierre Borne,et al.  EVOLUTIONARY ALGORITHMS FOR JOB-SHOP SCHEDULING , 2004 .

[10]  R. Szlapczynski Evolutionary Sets Of Safe Ship Trajectories: A New Approach To Collision Avoidance , 2010, Journal of Navigation.

[11]  C. Su,et al.  Decision Support from Genetic Algorithms for Ship Collision Avoidance Route Planning and Alerts , 2010 .

[12]  Bryant A. Julstrom,et al.  Codings and operators in two genetic algorithms for the leaf-constrained minimum spanning tree problem , 2004 .

[13]  Dr. Zbigniew Michalewicz,et al.  How to Solve It: Modern Heuristics , 2004 .

[14]  Piotr Skrzypczynski,et al.  A biologically inspired approach to feasible gait learning for a hexapod robot , 2010, Int. J. Appl. Math. Comput. Sci..

[15]  Lech Józwiak,et al.  Genetic engineering versus natural evolution: Genetic algorithms with deterministic operators , 2002, J. Syst. Archit..

[16]  Xiaoming Zeng,et al.  Evolution of the safe path for ship navigation , 2003, Appl. Artif. Intell..

[17]  Ming-Cheng Tsou,et al.  THE STUDY OF SHIP COLLISION AVOIDANCE ROUTE PLANNING BY ANT COLONY ALGORITHM , 2010 .

[18]  Rafal Szlapczynski Evolutionary Sets of Safe Ship Trajectories: Improving the Method by Adjusting Evolutionary Techniques and Parameters , 2011, ICCCI.

[19]  Rafal Szlapczynski,et al.  A Unified Measure Of Collision Risk Derived From The Concept Of A Ship Domain , 2006, Journal of Navigation.

[20]  Byung Suk Lee,et al.  Automatic collision avoidance of ships , 2009 .

[21]  Pedro Larrañaga,et al.  Evolutionary computation based on Bayesian classifiers , 2004 .

[22]  Zdzisław Kowalczuk,et al.  Niching mechanisms in evolutionary computations , 2006 .

[23]  Anup Kumar Panda,et al.  Potential field method to navigate several mobile robots , 2006, Applied Intelligence.

[24]  Olgierd Unold,et al.  Self-adaptation of parameters in a learning classifier system ensemble machine , 2010, Int. J. Appl. Math. Comput. Sci..