Webpage of Amiram Moshaiov (March 2016)
Amiram (Ami) Moshaiov
School of Mechanical Engineering
The Iby and Aladar Fleischman Faculty of Engineering
Ramat-Aviv, Tel-Aviv 69978, Israel
e-mail: email@example.com ,Tel. (work): 972-3-6407098
Our Computational Intelligence Research Group
A. What is Computational Intelligence (CI)?
CI deals with bio-inspired computational methods. Its roots are in Cybernetics (http://www.asc-cybernetics.org/foundations/definitions.htm ) and Soft-computing (https://en.wikipedia.org/wiki/Soft_computing).
While soft computing has focused on Artificial Neural Networks (ANNs), Fuzzy Logic (FL), and Evolutionary Computation (EC), CI has a much wider scope. In addition to the above three building blocks, CI deals with other bio-inspired methods such as Swarm Intelligence and Artificial Immune Systems. CI encompasses not only such bio-inspired computational techniques, but also their hybridizations.
Although developed somewhat separately from Artificial Intelligence (AI), CI may be considered as a branch of AI.
CI methods are currently in use in many technical systems and are consistently explored by researchers all over the world. Such activities are in the focus of the IEEE Society of Computational Intelligence.
CI applicability to robotics and engineering design makes it a valid topic for teaching mechanical engineering students. Such a course is provided by our school for both undergraduate and graduate students.
B. Overview on our Research
Our research group focuses on Evolutionary Computation (EC) methods. It also concerns their hybridization with Artificial Neural-Networks. Such hybridization is commonly termed Neuroevolution or Evolutionary Neural-Networks.
Concerning EC, we develop generic methods and algorithms with a focus on multi-objective problems. Such problems are common in engineering and other application areas (See https://en.wikipedia.org/wiki/Multi-objective_optimization ). Clearly, in the design of technical systems one must face with tradeoffs that are imposed by the existence of conflicting design objectives. Evolutionary algorithms are known to be most useful for finding the Pareto-optimal solutions to such problems. Such a solution approach, to multi-objective problems, reveals the performance tradeoffs, which supports a rational selection of a solution.
In 2014 we have extended our interest in multi-objective optimization to the related problem of competitive multi-objective games (see more at subsection C.2 below).
Although not focusing on neuro-fuzzy methods, since 2014 we have started to get interested in such an approach (See more at subsection C.3 below).
C. Uniqueness of our Research
1. The Concept-based Search and Optimization Approach: Traditional optimization aims at finding optimal solutions. In contrast, the concept-based search and optimization approach aims to support the selection of conceptual solutions. Its development has been strongly connected with Pareto-based multi-objective optimization and multi-criteria decision-making.
For over a decade we have been leading the development of the concept-based approach. We have uniquely developed a relaxed-Pareto version of the concept-based approach. It was motivated by the need to overcome the logical flaw that we have found in the original s-Pareto version of the concept-based approach (see explanation in Moshaiov et al. 2015 [ý6]).
Our relaxed-Pareto version of the concept-based approach can be used not only for concept selection, but also for the exploration of design spaces, both at the conceptual and particular design levels (Moshaiov et al. 2015 [ý6]).
In collaboration with Dr. Ohad Gur of the Israel Aerospace Industries, we have demonstrated the practicality of our concept-based approach to the industry.
2. Competitive Multi-objective Games: In collaboration with Dr Gideon Avigad, a former PhD student of our group, and others, we have developed a unique non-utility approach to multi-objective games, which are also known as multi-payoff games (Eisenstadt et al. 2016 pdf). At present we are investigating the capability of our non-utility approach to support decision-makers in various application areas such as defence (e.g., cyber security) and operation research.
3. Neuro-fuzzy Learning of the Behavior of Biological Systems: In collaboration with Prof. Amir Ayali, we have developed a neuro-fuzzy approach that can uniquely support the understanding of biological systems (Segal et al. 2016 [ý3]).
4. Family Bootstrapping: Commonly, evolutionary computation process is initiated with a random population of solutions. Yet, for complex problems, such an approach may fail to get started without taking proper measures. This phenomenon is known as the bootstrap problem. To overcome the problem, we have suggested a genetic transfer learning approach, which we have termed family bootstrapping (Moshaiov and Tal 2014 [ý9]).
While other methods exist to cope with the bootstrap problem, our unique family bootstrapping approach may facilitate revolutionizing the evolutionary design of robots. This is due to the use of relations among tasks. Family bootstrapping may lead to the creation of databases of useful ancestries, which will eliminate, or at least reduce, the need to initiate evolution processes from random.
5. Multi-objective Neuroevolution: We have been one of the first groups to consider the application of evolutionary multi-objective optimization for neuroevolution, and in particular for robot neuro-controllers. With this respect, we have been the first to propose an algorithm for multi-objective evolution of CPNs, i.e., counterpropagation networks (Moshaiov and Zadok 2013 [ý14]). We have also developed a unique MO-TWEANN, i.e., Multi-Objective version of the famous NEAT algorithm, for Topology and Weight Evolving Artificial Neural Network (Abramovich and Moshaiov 2016 [ý1]).
6. Multi-Competence Cybernetics: We have been the first to postulate on the significance of Pareto-optimality to the study of biological systems (Moshaiov 2008 [pdf]). A few years passed and in 2012 researchers from the Weizmann Institute of Science published a paper in Science confirming our suggestion J. (See Shoval et al., Science 336, 1157, 2012).
D. For Prospective MSc/PhD Students & Post-docs
If you are interested in topics such as: Systems (technical or biological), Engineering Design, Robotics, Mechtronics, Control, Artificial Intelligence, Bio-inspired Computational Methods such as Evolutionary Computations, Neural Networks, and Fuzzy Logic, then you may want to consider joining our research group.
In such a case, please read the above description on the uniqueness of our research, and see if it fits your interests. You may find a description of our group in Hebrew here [pdf].
E. List of Current Students
Erella Matalon-Eisenstadt (PhD)
David Alkaher (PhD)
Meir Harel (PhD)
Eliran Farhi (prospective PhD)
Omer Abramovitch (MSc)
Alon Snir (MSc)
Barak Samina (MSc)
Gil Segal (MSc)
Adham Salih (MSc)
Amir Arbel (MSc)
Alex Raki (MSc)
Roi Chananel (MSc)
NOTE! For copies of reports/papers, which are not linked here, please contact firstname.lastname@example.org
2. Moshaiov, A. The Paradox of Multimodal Optimization: Concepts vs. Species in Single and Multi-objective Problems. To appear in the Proceedings of the IEEE Congress on Evolutionary Computation, 2016. [pdf]
3. Segal, G., Moshaiov, A., Amichay G., and Ayali, A. Neuro-fuzzy Learning of Locust's Marching in a Swarm. To appear in the Proceedings of the Int. Joint Conference on Neural Networks, 2016.
4. Alkaher, D. and Moshaiov, A. Game-based Safe Aircraft Navigation in the Presence of Energy-Bleeding Coasting-Missile. To appear in the AIAA Journal of Guidance, Control, and Dynamics, 2016.
5. Snir, A., Samina, B., and Moshaiov, A. Concept-Based Evolutionary Multi-Criteria Exploration of Design Spaces Under Run-Time Limitation. Proceedings of the IEEE Symposium Series on Computational Intelligence, 2015.
6. Moshaiov, A., Snir, A. and Samina, B. Concept-based Evolutionary Exploration of Design Spaces by a Resolution-Relaxation-Pareto Approach. Proceedings of the IEEE Congress on Evolutionary Computation, 2015
7. Eisenstadt, E., Moshaiov, A. and Avigad, G. Co-evolution of Strategies for Multi-objective Games under Postponed Objective Preferences. Proceedings of the IEEE Conference on Computational Intelligence and Games, 2015.
8. Alkaher, D. and Moshaiov, A. Dynamic-Escape-Zone to Avoid Energy-Bleeding Coasting-Missile,” AIAA Journal of Guidance, Control, and Dynamics, 2015.
9. Moshaiov, A. and Tal, A. Family Bootstrapping: a Genetic Transfer Learning Approach for Onsetting the Evolution for a Set of Related Robotic Tasks. Proceedings of the IEEE Congress on Evolutionary Computation, 2014.
10. Moshaiov, A. and Abramovich, O. Is MO-CMA-ES Superior to NSGA-II for the Evolution of Multi-objective Neuro-controllers? Proceedings of the IEEE Congress on Evolutionary Computation, 2014.
11. Alkaher, D., Moshaiov, A., and Or, Y. Guidance Laws based on Optimal Feedback Linearization Pseudo-Control with Time-to-go Estimation. AIAA Journal of Guidance, Control and Dynamics. doi: 10.2514/1.G000205. 2014.
12. Moshaiov, A. and Elias, M. Variable-based ϵ – PAES with Adaptive Fertility Rate. Proceedings of the 13th Annual UK Workshop on Computational Intelligence (UKCI 2013), Guildford, UK, 2013.
13. Moshaiov, A. and Rizakov, Y. Signaling and Visualization for Interactive Evolutionary Search and Selection of Conceptual Solutions. Proceedings of GECCO WS on Visualization in Genetic and Evolutionary Computation (VizGEC 2013 at GECCO 2013), Amsterdam, The Netherlands, 2013.
14. Moshaiov, A. and Zadok, M. Evolving Counter-propagation Neuro-controllers for Multi-objective Robot Navigation. Proceedings of the 16th European Conference on EvoApplications, Lecture Notes in Computer Science, LNCS 7835, pp 589-598, 2013.
15. Israel, S., and Moshaiov, A. Bootstrapping Aggregate Fitness Selection with Evolutionary Multi-Objective Optimization. Parallel Problem Solving from Nature (PPSN XII), Lecture Notes in Computer Science, Volume 7492, pp 52-61, 2012.
16. Moshaiov, A. and Snir, Y. Tailoring ε-MOEA to Concept-based Problems. Parallel Problem Solving from Nature (PPSN XII), Lecture Notes in Computer Science, Volume 7492, pp 122-131, 2012.
17. Moshaiov, A., and Zadok, M. Evolution of CPN Controllers for Multi-objective Robot Navigation in Various Environments. Proceedings of the International Workshop on Evolutionary and Reinforcement Learning for Autonomous Robot Systems, (ERLARS 2012), Montpellier, France, 2012.
B. Book Chapters
1. Moshaiov, A. Multi-competence Cybernetics: The Study of Multi-objective Artificial Systems and Multi-fitness Natural Systems. In Multiobjective Problem Solving from Nature. Springer Berlin Heidelberg, pp. 285-304, 2008.[pdf]
B. Work-in-progress & Reports
1. Eisenstadt, E., and Moshaiov, A. Modelling of a Multi-objective Attack-Defence Cyber Security Game. 2016.ý [ýpdf]
2. Eisenstadt, E., Avigad, G., Moshaiov, A., and Branke, J. Rationalizable Strategies in Multi-Objective Games under Undecided Objective Preferences. 2016. [pdf
3. Eisenstadt, E., Moshaiov, A., and Avigad, G. Testing and Comparing Multi-objective Evolutionary Algorithms for Multi-payoff Games. 2016. ý [pdf]
4. Eisenstadt, E., Moshaiov, A., and Avigad, G. The Competing Selective Travelling Salespersons Problem under Multi-criteria. 2016. ý[pdf]
5. Samina, B., Snir, A., and Moshaiov, A. Interactive Evolutionary Multi-objective Exploration with A-priori Partition of the Searched Set. 2016. [pdf]
6. Salih, A. and Moshaiov, A. Multi-objective Neuroevolution: Should the Main Reproduction Mechanism be Crossover or Mutation? 2016.
7. Eisenstadt, E., Avigad, G., Moshaiov, A., and Branke, J. Multi-Objective Zero-Sum Games with Postponed Objective Preferences. 2014. [pdf]
My Professional Experience
A. Academic Experience
During 1985-88, Amiram Moshaiov was an Assistant Professor at MIT, USA.
Since 1988 A. Moshaiov has been at Tel-Aviv University (TAU).
He is the founder and developer of the robotics and mechatronics section of the Mech. Eng. Program at TAU.
B. Engineering Experience
Moshaiov served for 5 years as a mechanical engineer in the Israeli Navy.
While leading the robotics and mechatronics labs of the Mechanical Engineering Program at TAU, during the 90's, he was involved in the design of robots and their subsystems.
C. Activities in International Boards and Committees
Member of the Editorial Board of the Journal of Memetic Computing and a reviewer of many other scientific journals
Member of the IEEE Working Group on Artificial Life and Complex Adaptive Systems
Originator and Co-Chair of the IEEE/RSJ IROS Workshop on Multi-Objective Robotics
Originator and Co-Chair of the IEEE/RSJ IROS Workshop on Multi-Competence Optimization and Adaptation in Robotics and A-life
Originator and Co-Chair of the GECCO Workshop on the Evolution of Natural and Artificial Systems
Past member of the Management Board of the European Network of Excellence in Robotics
He is/was a member of many international program committees of conferences such as:
The IEEE International Conference on Systems, Man, and Cybernetics
The IEEE/RSJ International Conference on Intelligent Robots and Systems
The IEEE Congress on Evolutionary Computation
The IEEE World Congress on Computational Intelligence
The IEEE Symposium on Artificial Life
The IEEE Symposium on Comp. Intelligence for Security and Defense Applications
The IEEE Symposium on Comp. Intelligence in Multicriteria Decision Making
The International Conference on Parallel Problem Solving from Nature
The International Conference on Simulated Evolution and Learning
The European Robotic Symposium
The International IFAC Symposium on Robot Control
The International Symposium on Tools and Methods of Competitive Engineering
The International Conference on Engineering Design
The International Conference on Mechatronics
The IEEE International Conference on Control Applications
The IEEE International Conference on Computational Cybernetics
My Current Research Interests
A. Computational Intelligence Methods
Evolutionary Computation (e.g., Genetic Algorithms, Evolution Strategies)
Neural Networks (e.g., deep neural-networks)
Hybrid Methods (e.g., Evolutionary Neural Networks, Neuro-Fuzzy Inference Systems)
Interactive Evolutionary Computation
Evolutionary Multi-Objective Optimization
Hybrids of Evolutionary Multi-Objective Optimization and Multi-criteria Decision-making methods
Evolutionary Multi-Objective Adaptation
B. Other Techniques
Pontryagin Maximum Principle
C. Application Areas and Problems
Robotics (e.g., Behavioral, Cognitive, Developmental, Evolutionary)
Guidance and Control
Mechatronics (e.g., Simultaneous evolution of mechanics and control)
Complex Adaptive Systems
Cybernetics (e.g., Metaphors and analogies in natural and artificial Evolution, Multi-competence cybernetics)
Artificial Life (e.g., Bio-plausible simulations, System identification of the behavior of animals)
Computer Vision (e.g., Object detection and recognition, Bio-inspired vision systems, Attention)
Multi-Objective Games (known also as multi-payoff games or vector payoff games)
Aeronautical Games (e.g., Air combat problems, Pursuit-evasion)
Cyber Security Games
Games in Operation Research (e.g., Competitive travelling salespersons problems)
Engineering Design (e.g., Conceptual, Multi-objective, Robust, Interactive, Design space exploration)
Machine Learning (e.g., Supervised and unsupervised learning, Reinforcement learning, Deep learning, Transfer learning)
Artistic Design by Evolutionary Computation
My Past Research Topics
A. Moshaiov published during the 80’s and early 90’s in international scientific journals on many topics in structural mechanics, and shipbuilding manufacturing processes.
Later he conducted research on topics such as MEMS manufacturing processes, bio-mechanics of tennis elbow, and robot kinematics. For his current research topics, see above.