Webpage of Amiram )Ami) Moshaiov (Jan2020)


Amiram (Ami) Moshaiov

1.     School of Mechanical Engineering, Iby and Aladar Fleischman Faculty of Engineering

2.     Sagol School of Neuroscience

Tel-Aviv University

Ramat-Aviv, Tel-Aviv 69978, Israel

e-mail: moshaiov@tauex.tau.ac.il , Tel. (work): 972-3-6407098


             I.                        Our Computational Intelligence Research Group


A.              What is Computational Intelligence (CI)?

CI deals with bio-inspired computational methods. Its roots are in Cybernetics and in Soft-computing. See:

(http://www.asc-cybernetics.org/foundations/definitions.htm ) (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 of our Research

Our research group focuses on Evolutionary Computation (EC) methods, and in particular on Evolutionary Multi-objective Optimization (EMO). We also deal with hybridizations of ANNs and FL with EC. For example, we deal with Neuroevolution, which is also known as Evolutionary Neural-Networks.

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.

We have extended single and multi-objective optimization to Multi-Concept Optimization, which has a wide scope of applications as explained in Subsection C.1 below.

In 2014 we have extended our interest in multi-objective optimization to the related problem of non-cooperative multi-objective games, which are also known as multi-payoff Games (see Subsection C.2 below). Other related problems that we have dealt with are described in Subsections C.3-C.5) below. Related publications can be found in Sections II.A-II.D, below.


C.              Uniqueness of our Research


Note: See the “List of Recent Articles” in Section II below for exemplary articles on the following topics


1.     Multi-Concept Search and Optimization

Traditional optimization aims at finding optimal solutions. In contrast, the original aim of multi-concept search and optimization has been 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 multi-concept optimization. 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.

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, and as an alternative to multi-modal optimization.

In collaboration with Dr. Ohad Gur of the Israel Aerospace Industries, we have demonstrated the practicality of our concept-based approach to the industry.

Since 2018, we have presented a Tutorial on Multi-concept Optimization at international conferences including: IEEE-CEC 2018 and IEEE-SMC 2019. Next presentation is expected at GECCO-2020. See:

( https://gecco-2020.sigevo.org/index.html/Tutorials#id_Multi-concept%20Optimization ).

2.     Non-cooperative 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. At present we are investigating the capability of our non-utility approach to support decision-makers in various application areas such as defense (e.g., cyber security), operation research, economics and biology.

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.

4.     Family Bootstrapping by Genetic Transfer Learning

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.

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/Many-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. 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, also known as Neural Architecture Search (NAS). Recently we have developed a unique many-objective TWEANN algorithm to cope with an increasing number of objectives (4 and beyond).

6.     Multi-Competence Cybernetics

We have been the first to postulate on the significance of Pareto-optimality to the study of biological systems (See: https://link.springer.com/chapter/10.1007/978-3-540-72964-8_14 ). 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. List of Application Areas (for which we are currently applying our CI tools)


Engineering Design, Robotics, Control, Aeronautics, Cyber Security, Communication, Agriculture, Computer Vision and Classification

Operation Research and Management, Economy

Ecology, Zoology


E. Current International and National Collaboration


1.     AI to the Rescue: Life-and-Death Decision-Making under Conflicting Criteria – Funded by the Volkswagen Foundation.

In collaboration with S. Mustaghim, Otto von Guericke University, Germany and B. Adini, Faculty of Medicine at Tel-Aviv University.

2.     Drought-related Functional Tradeoffs of Trees – In collaboration with the Int. Institute for Applied System Analysis (IIASA), Austria.

3.     Multi-concept Optimization of Robotic Manipulators – In collaboration with the Agricultural Research Organization, Volcani Center, Israel.

4.     Non-cooperative Multi-payoff Games in Economics – In collaboration with the Economic Department, LUISS University, Italy.       


F. Other Research Projects/Research Topics of Interest


1.     Adversarial Robotics under Conflicting Objectives – Funded by the Israeli Ministry of Science.

2.     Communication Network Design by Multi-Concept Optimization under Conflicting Objectives – Funded by the Israeli Defense Ministry.

3.     Deep ANNs and Ensembles of ANNs by Many-objective Evolution of the Networks' Architecture and Weights.

4.     Multi-objective Genetic Transfer for Optimization.

5.     Metaheuristics and Adaptive Parameterization for Multi-objective Optimization and Multi-concept Optimization.

6.     Model Learning for Multi-concept Optimization.

7.     Machine Learning with Many-objective Optimization as Applied to Big Data.

8.     Revisiting Multi-concept Optimization and Multi-objective Games for the understanding of Biological Systems


G. 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.


H. List of Current Students


Meir Harel (PhD)

Eliran Farhi (PhD)

Adham Salih (PhD)

Ofer Weiss (PhD)

Yoni Maor (PhD)

Joseph Gabbay (PhD)

Stav Bar-Sheshet (PhD)

Tamir Mhabari (MSc)

Yossef Braslev (MSc)

Yuval Alon (BSc)


I. List of Recent Graduates


Erella Matalon-Eisenstadt (PhD, 2019)

David Alkaher (PhD, 2019)

Barak Samina (MSc, 2019)

Roi Chananel (MSc, 2019)

Adham Salih (MSc, 2018)

Gil Segal (MSc, 2017)

Omer Abramovich (MSc, 2017)


      II.                        List of Recent Articles


Note: For copies of reports/papers, which are not linked here, please contact moshaiov@tauex.tau.ac.il


A.    Articles on Multi-objective Optimization and Multi-Concept Optimization


1.     Farhi, E. and Moshaiov, A. Window-of-Interest-based Multi-objective Evolutionary Search for Satisficing Concepts

Proceedings of the IEEE Conference on Systems, Man and Cybernetics, 2017.

2.     Moshaiov, A. The Paradox of Multimodal Optimization:  Concepts vs. Species in Single and Multi-objective Problems

Proceedings of the IEEE Congress on Evolutionary Computation, 2016.

3.     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.

4.     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.


B.    Articles on Multi-objective Neuroevolution


5.     Salih, A. and Moshaiov, A. Many-objective Topology and Weight Evolution of Neural-Networks,

Submitted to the International Joint Conference on Neural Networks, 2020.

6.     Abramovich, O. and Moshaiov, A. Multi-objective Topology and Weight Evolution of Neuro-controllers,

Proceedings of the IEEE Congress on Evolutionary Computation, 2016. 

7.     Salih, A. and Moshaiov, A. Multi-objective Neuroevolution: Should the Main Reproduction Mechanism be Crossover or Mutation?

Proceedings of the IEEE Conference on Systems, Man and Cybernetics, 2016.

8.     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.

9.     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.


C.    Articles on Fuzzy and Neuro-fuzzy Control


10. Gabbay, J., and Moshaiov, A. Human-like Motion Control for Autonomous Driving in Rugged Terrains,

To be submitted to the IEEE International Conference on Fuzzy Systems, 2020.

11. Segal, G., Moshaiov, A., Amichay G. and Ayali, A. Neuro-fuzzy Learning of Locust's Marching in a Swarm.

Proceedings of the International Joint Conference on Neural Networks, 2016.


D.   Articles on Single and Multi-objective Games


12. Eisenstadt, E. and Moshaiov, A. Co-Evolutionary Algorithm for Solving Multi-Objective Games,

To be submitted to Swarm and Evolutionary Computation

13. Eisenstadt, E. and Moshaiov, A. Mutual Rationalizability in Vector-payoff Games,

Proceedings of the International Conference on Evolutionary Multi-Criterion Optimization, 2019.

14. Harel, M., Moshaiov, A. and Alkaher, D. Rationalizable Strategies for the Navigator-Target-Missile Game,

Accepted for publication in the AIAA Journal of Guidance, Control, and Dynamics, 2019.

15. Eisenstadt, E. and Moshaiov, A. Decision‐making in Non‐cooperative Games with Conflicting Self‐objectives,  

Journal of Multi‐Criteria Decision Analysis, 2018.

16. Alkaher, D. and Moshaiov, A. Non-dominated Strategies for Cautious to Courageous Aerial Navigation,

AIAA Journal of Guidance, Control, and Dynamics, 2018.

17. Eisenstadt, E. and Moshaiov, A. Novel Solution Approach for Multi-objective Attack-Defense Cyber Games with Unknown Utilities of the Opponent, IEEE Transactions on Emerging Topics in Computational Intelligence, 2017.

18. Harel, M., Eisenstadt, E. and Moshaiov, A. Solving Multi-objective Games using A-priori Auxiliary Criteria,

Proceedings of the IEEE Congress on Evolutionary Computation, 2017. 

19. Eisenstadt, E., Moshaiov, A. and Avigad G. The Competing Travelling Salespersons Problem under Multi-criteria,

Proceedings of the International Conference on Parallel Problem Solving from Nature, 2016.

20. Alkaher, D. and Moshaiov, A. Game-based Safe Aircraft Navigation in the Presence of Energy-Bleeding Coasting-Missile.

AIAA Journal of Guidance, Control, and Dynamics, 2016.

21. Alkaher, D. and Moshaiov, A. Dynamic-Escape-Zone to Avoid Energy-Bleeding Coasting-Missile,”

AIAA Journal of Guidance, Control, and Dynamics, 2015.

22. 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 (CIG), pp. 461–468, 2015.

21. 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, 2014.


  III.                        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 Editorial Boards, International Organizations and Conferences


Associate Editor of the IEEE Transaction on Emerging Topics in Computational Intelligence

Member of the Editorial Board of the Journal of Memetic Computing and a reviewer of many other scientific journals

Member of the IEEE Task Force on Artificial Life and Complex Adaptive Systems

Member of the IEEE Task Force on Evolutionary Deep Learning and Applications

Member of the EURO Working Group on Multicriteria Decision Aiding

Ex-member of the Management Board of the European Network of Excellence in Robotics

Moshaiov 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


D. Organization of Workshops and Special Sessions at International Conferences


                  Originator and Co-Chair of:

The IEEE/RSJ IROS Workshop on Multi-Objective Robotics

The IEEE/RSJ IROS Workshop on Multi-Competence Optimization and Adaptation in Robotics and A-life

The GECCO Workshop on the Evolution of Natural and Artificial Systems

The Special Session on EC and Games at IEEE-CEC 2017



IV.                        My Current Research Interests


A. Computational Intelligence Methods


Evolutionary Computation (e.g., Genetic Algorithms, Evolution Strategies)

Neural Networks (e.g., deep neural-networks)

Fuzzy Logic

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


Genetic Transfer

Learning, Adaptation, Development


B. Other Techniques


Pontryagin Maximum Principle

Differential Games

Feedback Linearization


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)

Multi-objective Planning

Multi-objective Navigation

Machine Learning (e.g., Supervised and unsupervised learning, Reinforcement learning, Deep learning, Transfer learning)

Text Analysis

Multi-Agent Systems

Artistic Design by Evolutionary Computation



       V.                        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.