Publications·Research·Thesis·Contact details·CV

As of 2008 I've been working full-time for an innovative data-mining company called Adapticon (www.adapticon.com).

While I'm still affiliated with Utrecht University, I'm currently unable to accept any requests for internships, program committee memberships, etc. Thanks for your understanding.

- I co-organize(d) these events:

BNAIC-2007: The annual Belgian-Dutch AI conference. CFP

AAAI Fall Symposium 2007: Computational Approaches to Representation Change During Learning and Development.

GECCO-06 Workshop on Adaptive Representations

GECCO-06 Advanced tutorial on Coevolution

AAMAS-06 Workshop on Hierarchical Autonomous Agents and Multi-Agent Systems

AAAI 2005 Fall Symposium on Coevolutionary and Coadaptive Systems

GECCO-05 Coevolution Discussion Forum

GECCO-05 Workshop on Theory of Representations

GECCO-04 Workshop on Modularity, Regularity, and Hierarchy in Evolutionary Computation

ICML-02 Workshop on Development of Representations

- I serve(d) on the program committee of:

ECAL 2007

CEC-06

ECML/PKDD 2006

GECCO-06 , Coevolution track

CIG-06: IEEE Symposium on Computational Intelligence and Games

AAMAS-06 ALAAMAS Workshop

EuroGP-06: 9th European Conference on Genetic Programming

BNAIC 2006 · ECML-PKDD ws on multi-agent learning 2005

EMO-05 · EuroGP-05 · ECAL-05 ws on Active Agents

BNAIC 2005 · GECCO-04 · EuroGP-04 · GECCO-03 ADoRO ws · EWLR 6, 7, 8, 9.

- Resources:

We have set up a Coevolution Wiki in which coevolution terms are discussed.

The HPG: a generator for hierarchical problems

Coevolution website: [Coevolution] · [Accurate Evaluation in Coevolution] · [The DELPHI Algorithm]

- Board member of the BNVKI
- Chair of the GECCO 2005 Coevolution Track

- Popovici, E., Bucci, A., Wiegand, P. and De Jong E.D. (2010). Coevolutionary Principles [PDF]. Appears in: Rozenberg, G; Baeck, T.; Kok, J.N. (Eds.)
*Handbook of Natural Computing*, Springer-Verlag, Berlin, 2010. This is the author-generated final version, provided with special permission from Springer. The official SpringerLink version is available here.

- De Jong, E.D. and A. Bucci (2008). Objective Set Compression. Joshua Knowles, David Corne, Kalyanmoy Deb and Deva Raj Chair (Eds.) Multiobjective Problem Solving from Nature. See the abstract

- P.A.N. Bosman and E.D. de Jong. Adaptation of a Success Story in GAs: Estimation-of-Distribution Algorithms for Tree-based Optimization Problems. In A. Yang and Y. Shan, editors, Success in Evolutionary Computation, pages 3-18, Springer-Verlag, Berlin, 2008.

- De Jong, E.D. and De Boer, B. Dynamical Systems, Individual-Based Modeling, and Self-Organization. Unesco EOLSS Encyclopedia, Artificial Intelligence theme.

- De Jong, E.D. A Monotonic Archive for Pareto-Coevolution.
*Evolutionary Computation*, The MIT Press.

- Franke, L., H. van Bakel, L. Fokkens, E.D. de Jong, M. Egmont-Petersen, and C. Wijmenga (2006).
Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes.. American Journal of Human Genetics, vol. 78, pp. 1011-1025.

- De Jong, E.D. and J.B. Pollack (2004). Ideal Evaluation from Coevolution [PS] [PDF]. This is the final manuscript of the article. The final version of this article is published in
*Evolutionary Computation*, Vol. 12, Issue 2, pp. 159-192, published by The MIT Press. See DELPHI algorithm webpage, including Matlab(R) version and online demo.

- De Jong, E.D. and Pollack, J.B. (2003). Multi-Objective Methods for Tree Size Control [PS] [PDF]
*Genetic Programming and Evolvable Machines*, vol. 4, no. 3, pp. 211-233.**Note:**this is a correct version of the article. Unfortunately, several typesetting errors occur in the published version; see erratum [PS] PDF].

- De Jong, E.D. and L. Steels (2003). A Distributed Learning Algorithm for Communication Development [PS] [PDF].
*Complex Systems*, vol. 14, no. 4, pp. 315-334. See online appendix, including Matlab(R) implementation of the algorithm.

- De Jong, E.D., L. Franke, and A.P.J.M. Siebes (2006). A comparison of gene interaction measures. RECOMB 2006.

- De Jong, E.D., and A.P.J.M. Siebes (2006). Evaluation of a Gene Network Extraction Method on Synthetic Data. Proceedings of ISNB 2006.

- Van Wijngaarden, R.P.T., and E.D. de Jong (2008). Evaluation and Diversity in Co-evolution. Parallel Problem Solving from Nature, PPSN X.

- Edwin D. de Jong (2007). Objective Fitness Correlation. Best paper award, coevolution track. Proceedings of the 9th annual conference on Genetic and evolutionary computation, GECCO-07.

- T-S. Yo and E.D. de Jong (2007). A comparison of evaluation methods in coevolution. Proceedings of the 9th annual conference on Genetic and evolutionary computation, GECCO-07.

- Wiering, M.A., and E.D. de Jong (2007). Computing Optimal Stationary Policies for Multi-Objective Markov Decision Processes. 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning.

- E.D. de Jong, L. Franke, and A. Siebes (2007). On the Measurement of Genetic Interactions. Proceedings of the 3rd international symposium on Computational Life Science, CompLife 2007.

- van de Koppel, E., Slavkov, I., Astrahantseff, K., Schramm, A., Schulte, J., Vandesompele, J., de Jong, E., Dzeroski, S., Knobbe, A. (2007). Knowledge Discovery in Neuroblastoma-related Biological Data, Data Mining in Functional Genomics and Proteomics workshop.

- De Jong, E.D., and A. Bucci (2006). DECA: Dimension Extracting Coevolutionary Algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-06.

- Oliehoek, F.A., E.D. de Jong, and N. Vlassis (2006). The Parallel Nash Memory for Asymmetric Games. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-06 (nominated for best paper award).

- Bosman, P.A.N., and E.D. de Jong (2006). Combining gradient techniques for numerical multi-objective evolutionary optimization. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-06 (nominated for best paper award).

- Snijders, P., E.D. de Jong, B. de Boer, and F. Weissing (2006). Multi-Objective Diversity Maintenance. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-06.

- De Back, W., and E.D. de Jong and M.A. Wiering (2006). Red Queen dynamics in a predator-prey ecosystem. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-06.

- Van Diggelen, J., E.D. de Jong, and M.A. Wiering (2006). Strategies for Ontology Negotiation: Finding the Right Level of Generality. International workshop on agent communication 2006 at AAMAS2006.

- Zwanepol Klinkmeijer, L., E.D. de Jong, and M. Wiering (2006). A serial population genetic algorithm for dynamic optimization problems. Proceedings of the Annual Machine Learning Conference of Belgium and The Netherlands, BeNeLearn-06.

- Oliehoek, F.A., N. Vlassis, and Edwin D. de Jong (2005). Coevolutionary Nash in Poker Games.
*Proceedings of the 17th Belgian-Dutch Conference on Artificial Intelligence, BNAIC-05, pp. 188-193.*

- De Jong, E.D., Richard A. Watson, and Dirk Thierens (2005). On the Complexity of Hierarchical Problem Solving [PDF] [PS] Proceedings of the Genetic and Evolutionary Computation Conference GECCO-05.
**Best paper award**, GA track.

- De Jong, E.D. (2005). The MaxSolve Algorithm for Coevolution [PDF][PS] Proceedings of the Genetic and Evolutionary Computation Conference GECCO-05.

- De Jong, E.D., Richard A. Watson, and Dirk Thierens (2005). A generator for Hierarchical Problems [PDF] [PS] GECCO Workshop on the Theory of Representations.

- Bosman, Peter A.N. and Edwin D. De Jong (2005). Exploiting Gradient Information in Numerical Multi-Objective Evolutionary Optimization [PDF] [PS]. Proceedings of the Genetic and Evolutionary Computation Conference GECCO-05.

- De Jong, E.D. (2005). Maximizing Expected Utility in Coevolutionary Search. Proceedings of the Annual Machine Learning Conference of Belgium and The Netherlands, BeNeLearn-05.

- De Jong, E.D., Dirk Thierens, and Richard A. Watson (2004). Hierarchical Genetic Algorithms [PS][PDF] Proceedings of the 8th International Conference on Parallel Problem Solving from Nature PPSN-04, pp.232-241.

- De Jong, E.D. (2004). Intransitivity in Coevolution [PS][PDF] Proceedings of the 8th International Conference on Parallel Problem Solving from Nature PPSN-04, pp. 843-851.

- 't Hoen, P.J. and E.D. de Jong (2004). Evolutionary Multi-Agent Systems [PS] [PDF]. Proceedings of the 8th International Conference on Parallel Problem Solving from Nature PPSN-04, pp. 872-881.

- Bosman, P.A.N. and E.D. de Jong (2004). Learning Probabilistic Tree Grammars for Genetic Programming [PDF]. Proceedings of the 8th International Conference on Parallel Problem Solving from Nature PPSN-04, pp. 192-201.

- De Jong, E.D. (2004). Towards a Bounded Pareto-Coevolution Archive [PS][PDF] Proceedings of the Congress on Evolutionary Computation CEC-04, pp. 2341-2348.

- De Jong, E.D. (2004). The Incremental Pareto-Coevolution Archive [PS][PDF] Proceedings of the Genetic and Evolutionary Computation Conference GECCO-04, pp. 525-536.

- Bucci, A., Pollack, J.B., and De Jong, E.D. (2004). Automated Extraction of Problem Structure [PS][PDF] Proceedings of the Genetic and Evolutionary Computation Conference GECCO-04, pp.501-512 (nominated for best paper award, coevolution track).

- De Jong, E.D. and Thierens, D. (2004). Exploiting Modularity, Hierarchy, and Repetition in Variable-Length Problems [PS][PDF] Proceedings of the Genetic and Evolutionary Computation Conference GECCO-04, pp. 1030-1040.

- Bosman, P.A.N. and De Jong, E.D. (2004). Grammar Transformations in an EDA for Genetic Programming. Proceedings of the GECCO-04 Workshop on Optimization by Building and Using Probabilistic Models, OBUPM-04.

- De Jong, E.D., Thierens, D., and Watson, R.A. (2004). Defining Modularity, Hierarchy, and Repetition. Proceedings of the GECCO Workshop on Modularity, regularity and hierarchy in open-ended evolutionary computation, pp.2-6.

- De Jong, E.D. (2004). Guaranteeing Progress in Pareto-Coevolution. Proceedings of the Annual Machine Learning Conference of Belgium and The Netherlands, BeNeLearn-04, pp.22-29.

- De Jong, E.D. (2003). Combining Exploration and Reliability in Coevolution [PS] [PDF] Proceedings of the Fifteenth Netherlands/Belgium Conference on Artificial Intelligence BNAIC'03, pp.179-186.
**Best Paper**award.

- De Jong, E.D. and Pollack, J.B. (2003). Learning the Ideal Evaluation Function [PS] [PDF] Proceedings of the Genetic and Evolutionary Computation Conference, coevolution track, GECCO-2003, pp. 277-288. Springer-Verlag, LNCS series. See Abstract.

- De Jong, E.D. (2003). Representation Development from Pareto-Coevolution [PS] [PDF] Proceedings of the Genetic and Evolutionary Computation Conference, coevolution track, GECCO-2003, pp. 265-276. Springer-Verlag, LNCS series. See Abstract.

- Peshkin, L. and De Jong, E.D. (2002). Context-based policy search: transfer of experience across problems [PS]. [PDF].
*Proceedings of the ICML-2002 Workshop on Development of Representations.*See Abstract.

- De Jong, E.D. and Oates, T. (2002). A Coevolutionary Approach to Representation Development [PS]. [PDF].
*Proceedings of the ICML-2002 Workshop on Development of Representations.*See Abstract.

- De Jong, E.D. and Pollack, J.B. (2001). Utilizing Bias to Evolve Recurrent Neural Networks. [PS] [PDF]. Proceedings of IJCNN 2001, vol.4, pp. 2667-2672. See Abstract.

- De Jong, E.D., Watson, R.A., and Pollack, J.B. (2001). Reducing Bloat and Promoting Diversity using Multi-Objective Methods [PS] [PDF]. Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2001, pp. 11-18. Spector, L., E. Goodman, A. Wu, W.B. Langdon, H.-M. Voigt, M. Gen, S. Sen, M. Dorigo, S. Pezeshk, M. Garzon, and E. Burke, eds. See Abstract.

- De Jong, E.D. (2000). Attractors in the Development of Communication [PS] [PDF]. J.-A. Meyer, A. Berthoz, D. Floreano, H. Roitblat, and S. Wilson (Eds).
*SAB2000 Proceedings Supplement Book*. Honolulu, Hawaii: International Society for Adaptive Behavior. This is a shorter version of AI-MEMO 00-02 [PDF]. See Abstract.

- De Jong, E.D. (1999). Analyzing the Evolution of Communication from a Dynamical Systems Perspective [PS] [PDF].
*Proceedings of the European Conference on Artificial Life ECAL'99*, 689-693. ©Springer-Verlag LNCS, Berlin. Click here for a Gzipped PostScript version. An extended version appeared as AI-MEMO 99-08 [PDF] (.ps.gz) of the VUB AI Lab. See Abstract.

- De Jong, E.D. (1999). Autonomous Concept Formation [PS] [PDF]. In T. Dean (ed.),
*Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence IJCAI'99*, 344-349. San Francisco, CA: Morgan Kaufmann Publishers. See Abstract.

- De Jong, E.D. and L. Steels (1999). Generation and selection of sensory channels [PS] [PDF].
*Evolutionary Image Analysis, Signal Processing and Telecommunications First European Workshops, EvoIASP'99 and EuroEcTel'99 Joint Proceedings, pp. 90-100*. Göteborg, Sweden, May 1999. ©Springer-Verlag LNCS 1596 , Berlin. Click here for a Gzipped PostScript version. See Abstract.

- De Jong, E.D. (1999). Coordination Developed by Learning from Evaluations [PS] [PDF]. J.A. Padget (ed.)
*Collaboration between Human and Artificial Societies*, ©Springer-Verlag LNAI Vol. 1624. Click here for a Gzipped PostScript version. See Abstract. An earlier version appeared in the Notes of the VIM'97 Workshop on Collaboration between human and artificial societies. Universita' di Salerno.

- De Jong, E.D. (1998). The Development of a Lexicon Based on Behavior [PS] [PDF]. Han La Poutré and Jaap van den Herik (editors)
*Proceedings of the Tenth Netherlands/Belgium Conference on Artificial Intelligence NAIC'98, pp. 27-36*. CWI, Amsterdam, The Netherlands. Click here for a Gzipped PostScript version. See Abstract.

- De Jong, E.D. and P. Vogt (1998). How
Should a Robot Discriminate Between Objects? [PS] [PDF] A comparison between two
methods.
*Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior SAB'98, pp. 86-91*. MIT Press. Cambridge, MA. Click here
for a Gzipped PostScript version. See Abstract. - De Jong, E.D. (1997). An
Accumulative Exploration Method for Reinforcement Learning [PS] [PDF]
*Notes of the AAAI'97 Workshop on Multiagent Learning*, AAAI technical report WS-97-03. Click here for a Gzipped PostScript version. See Abstract.

- De Jong, E.D. (1997). Multi-Agent
Coordination by Communication of Evaluations [PS] [PDF]
*Proceedings of the 8th European Workshop on Modelling Autonomous Agents in a Multi-Agent World*, MAAMAW'97, 1997. Magnus Boman and Walter Van de Velde, eds. Springer-Verlag, Berlin. Click here for a Gzipped PostScript version. See Abstract.

- De Jong, E.D., H. Keuken, E. van der Pol, E. den Dekker, and E.J.H. Kerckhoffs
(1996). Exergy Analysis of Industrial Processes using AI Techniques. In
*Computers chem. Engng Vol. 20, Suppl., pp.S1631-S1636*. Elsevier, Great Britain.

- De Jong, E.D., E. den Dekker, H. Keuken, E. van der Pol, and E.J.H. Kerckhoffs (1995).
Computation and Evaluation of Exergy Efficiencies to Assist the Development
of a Sustainable Future. In M. Dal Cin, A. Herzog, G. Bolck, and A. Riza Kaylan (editors)
*7th European Simulation Symposium, ESS '95*. Society for Computer Simulation International, Istanbul.

- De Jong, E.D. Modular Variable-length Representations from Pareto-Coevolution. Technical Report no. UU-CS-2003-009 [PS]. [PDF]. Institute of Information and Computing Sciences, Utrecht University. March 20, 2003.

- De Jong, E.D. and J.B. Pollack. Principled Evaluation in Coevolution. Technical Report no. CS-02-225 [PS], also available in single-spaced version. Computer Science Department, Brandeis University. May 31, 2002.

In most methods of evolutionary computation, a fixed fitness function is defined. This results in a particular gradient, and determines the course of evolution beforehand. If on the other hand the evaluation of individuals is based on interactions with other evolving individuals, then in principle a potential for open-ended evolution can arise. Evolutionary methods with this form of evaluation are called co-evolution. Coevolution is substantially different from conventional evolution, in that the ranking of individuals is dependent on the co-evolving individuals. An interesting recent development in coevolution is Pareto-coevolution, which views the interactions with other evolving entities as objectives, in the sense employed by Evolutionary Multi-Objective Optimization (EMOO).

If the design of complex systems is viewed as a process of arranging large numbers of primitive elements in a specific manner, then the space of possible designs is prohibitively large. However, if some choices concerning aspects of a design can be made independent of other choices, then the required size of the effective search space can be greatly reduced by recursively combining such independently made choices. A substantial reduction is still possible if choices are not independent, but if the dependencies are merely reduced compared to the case where each choice depends on all other choices. Problems exhibiting this property are called modular, and the principle of modularity is seen in a wide variety of design processes, ranging from nature to engineering and from music composition to software design. I am interested in seeing how it can be achieved by evolutionary processes. A particular direction I am exploring is to view the evolution of modules and the assembly thereof as a process of coevolution.

After receiving the MSc from Delft University of Technology, I went to the VUB AI Lab in Brussels, Belgium, headed by Prof. Luc Steels. The first topic I've worked on is coordination between software agents [3]. The idea behind this research is that if agents are to coordinate their actions with other agents whose competences are unknown in advance, they can achieve this coordination by sending evaluations to each other. This turned out to be a feasible idea [6], and led me to the interesting field of reinforcement learning. My research in that area included the *exploration/exploitation* dilemma [4], and *generalization* [5].

These earlier efforts led me to research concerning the development of language. Concepts are not assumed to be present in the agents already, but are developed based on interaction with the environment. A first result was that a shared lexicon can emerge in a community of agents that independently form private concepts about their environment [7]. As a next step, I have suggested the principle of *autonomous concept formation*, using which agents can form *situation concepts* [9]. A situation concept is a set of states of the environment that is relevant for an agent to distinguish from other possible states because the situation is predictive of future states. When agents build up a lexicon relating *situations* to *words*, they can use this lexicon to communicate information about the environment. Thus, uncertainty due to incomplete perception can be overcome by means of communication. In subsequent work, I investigated communication as a dynamical system in which associations between concepts and words continuously change [10], and perfect systems of communication are point attractors [11]. Details can be found in my PhD thesis.

Next, in August 2000, I went to Boston to do a two-year postdoc at Jordan Pollack's DEMO Lab. After these stays abroad, I'm back again in my home country: The Netherlands.

Organizations: AAAI, IEEE, ISAB, BNVKI, Evonet, Emergentia.