Gusz Eiben | Vrije Universiteit Amsterdam (original) (raw)

Papers by Gusz Eiben

Executive Summary Future and Emerging Technologies − Proactive Initiatives (FET − Proactive) is p... more Executive Summary Future and Emerging Technologies − Proactive Initiatives (FET − Proactive) is preparing the next work programme for 2013 and later. As part of the process to identify new research challenges, the European Commission organised a workshop on 10 November 2011, in Brussels, to brainstorm and elaborate novel candidates for future FET proactive initiatives in the area of Living Technologies. The meeting was attended by 12 researchers and covered a broad range of topics linked to this theme. The objective was to identify specific topics as suitable candidates for future proactive initiatives. In discussion following the presentations of the individual participants, the group identified a single broad challenge which they felt captured most of the key components of the individual challenges proposed. This single challenge was then further explored and elaborated as a potential seed theme for a future proactive initiative. The initial description offered here (detailed in S...

SoundICTs: Endowing human auditory capacities and embodying audition faculties into artificial sy... more SoundICTs: Endowing human auditory capacities and embodying audition faculties into artificial systems Ferran Cabrer I Vilagut...................................................................................... 14

In this chapter we describe evolutionary algorithms (EAs) for constraint handling. Constraint han... more In this chapter we describe evolutionary algorithms (EAs) for constraint handling. Constraint handling is not straightforward in an EA because the search operators mutation and recombination are “blind” to constraints. Hence, there is no guarantee that if the parents satisfy some constraints the offspring will satisfy them as well. This suggests that the presence of constraints in a problem makes EAs intrinsically unsuited to solve this problem. This should especially hold when the problem does not contain an objective function to be optimized, but only constraints – the category of constraint satisfaction problems. A survey of related literature, however, indicates that there are quite a few successful attempts to evolutionary constraint satisfaction. Based on this survey, we identify a number of common features in these approaches and arrive at the conclusion that EAs can be effective constraint solvers when knowledge about the constraints is incorporated either into the genetic o...

The Semantic Web has become a dynamic and enormous network of typed links between data sets store... more The Semantic Web has become a dynamic and enormous network of typed links between data sets stored on different machines. These data sets are machine readable and unambiguously interpretable, thanks to their underlying standard representation languages. The expressiveness and flexibility of the publication model of Linked Data has led to its widespread adoption and an ever increasing publication of semantically rich data on the Web. This success however has started to create serious problems as the scale and complexity of information outgrows the current methods in use, which are mostly based on data- base technology, expressive knowledge representation formalism and high-performance computing. We argue that methods from computa- tional intelligence can play an important role in solving these prob- lems. In this paper we introduce and systemically discuss the typical application problems on the Semantic Web and argue that the existing approaches to address their underlying reasoning...

2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020

The long term goal of the Autonomous Robot Evolution (ARE) project is to create populations of ph... more The long term goal of the Autonomous Robot Evolution (ARE) project is to create populations of physical robots, in which both the controllers and body plans are evolved. The transition for evolutionary designs from purely simulation environments into the real world creates the possibility for new types of system able to adapt to unknown and changing environments. In this paper, a system for creating robots is introduced in order to allow for their body plans to be designed algorithmically and physically instantiated using the previously introduced Robot Fabricator. This system consists of two types of components. Firstly, skeleton parts are created bespoke for each design by 3D printing, allowing the overall shape of the robot to include almost infinite variety. To allow for the shortcomings of 3D printing, the second type of component are organs which contain components such as motors and sensors, and can be attached to the skeleton to provide particular functions. Specific organ designs are presented, with discussion of the design challenges for evolutionary robotics in hardware. The Robot Fabricator is extended to allow for robots with joints, and some example body plans shown to demonstrate the diversity possible using this system of robot generation.

IEEE Transactions on Cognitive and Developmental Systems, 2020

2006 IEEE International Conference on Evolutionary Computation

In this paper we evaluate a new approach to selection in Genetic Algorithms (GAs). The basis of o... more In this paper we evaluate a new approach to selection in Genetic Algorithms (GAs). The basis of our approach is that the selection pressure is not a superimposed parameter defined by the user or some Boltzmann mechanism. Rather, it is an aggregated parameter that is determined collectively by the individuals in the population. We implement this idea in two different ways and experimentally evaluate the resulting genetic algorithms on a range of fitness landscapes. We observe that this new style of selection can lead to 30-40% performance increase in terms of speed.

Proceedings of the 15th annual conference companion on Genetic and evolutionary computation, 2013

Parameter control mechanisms in evolutionary algorithms (EAs) dynamically change the values of th... more Parameter control mechanisms in evolutionary algorithms (EAs) dynamically change the values of the EA parameters during a run. Research over the last two decades has delivered ample examples where an EA using a parameter control mechanism outperforms its static version with fixed parameter values. However, very few have investigated why such parameter control approaches perform better. In principle, it could be the case that using different parameter values alone is already sufficient and EA performance can be improved without sophisticated control strategies. This paper investigates whether very simple random variation in parameter values during an evolutionary run can already provide improvements over static values.

: The paper contains results of a research project aimed at application and evaluation of modernd... more : The paper contains results of a research project aimed at application and evaluation of moderndata analysis techniques in the field of marketing. The investigated techniques were: neural networks, evolutionaryalgorithms, CHAID and logistic regression analysis. All techniques were applied to the problem of makingoptimal selections for direct mailing and the resulting models were compared w.r.t. accuracy, interpretability,transparency and time and expertise needed for their construction.1...

... 32, Learning Short-Term Weights for GSAT Frank - 1997. 30, A new representation and operato... more ... 32, Learning Short-Term Weights for GSAT Frank - 1997. 30, A new representation and operators for genetic algorithms applied to grouping problems Falkenauer - 1994. 28, Solving constraint satisfaction problems using hybrid evolutionary search Bowen, J, et al. - 1998. ...

2020 IEEE Symposium Series on Computational Intelligence (SSCI)

The 2019 Conference on Artificial Life, 2019

The long term vision of the Autonomous Robot Evolution (ARE) project is to create an ecosystem of... more The long term vision of the Autonomous Robot Evolution (ARE) project is to create an ecosystem of both virtual and physical robots with evolving brains and bodies. One of the major challenges for such a vision is the need to construct many unique individuals without prior knowledge of what designs evolution will produce. To this end, an autonomous robot fabrication system for evolutionary robotics, the Robot Fabricator, is introduced in this paper. Evolutionary algorithms can create robot designs without direct human interaction; the Robot Fabricator will extend this to create physical copies of these designs (phenotypes) without direct human interaction. The Robot Fabricator will receive genomes and produce populations of physical individuals that can then be evaluated, allowing this to form part of the evolutionary loop, so robotic evolution is not confined to simulation and the reality gap is minimised. In order to allow the production of robot bodies with the widest variety of shapes and functional parts, individuals will be produced through 3D printing, with prefabricated actuators and sensors autonomously attached in the positions determined by evolution. This paper presents details of the proposed physical system, including a proof-ofconcept demonstrator, and discusses the importance of considering the physical manufacture for evolutionary robotics.

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021

In this work, we propose a novel approach for reinforcement learning driven by evolutionary compu... more In this work, we propose a novel approach for reinforcement learning driven by evolutionary computation. Our algorithm, dubbed as Evolutionary-Driven Reinforcement Learning (Evo-RL), embeds the reinforcement learning algorithm in an evolutionary cycle, where we distinctly differentiate between purely evolvable (instinctive) behaviour versus purely learnable behaviour. Furthermore, we propose that this distinction is decided by the evolutionary process, thus allowing Evo-RL to be adaptive to different environments. In addition, Evo-RL facilitates learning on environments with rewardless states, which makes it more suited for real-world problems with incomplete information. To show that Evo-RL leads to state-of-the-art performance, we present the performance of different state-of-the-art reinforcement learning algorithms when operating within Evo-RL and compare it with the case when these same algorithms are executed independently. Results show that reinforcement learning algorithms embedded within our Evo-RL approach significantly outperform the stand-alone versions of the same RL algorithms on OpenAI Gym control problems with rewardless states constrained by the same computational budget.

Lecture Notes in Computer Science, 2016

Experimental Methods for the Analysis of Optimization Algorithms, 2010

Advances in Artificial Life, ECAL 2013, 2013

Evolutionary robotics is heading towards fully embodied evolution in real-time and real-space. In... more Evolutionary robotics is heading towards fully embodied evolution in real-time and real-space. In this paper we introduce the Triangle of Life, a generic conceptual framework for such systems in which robots can actually reproduce. This framework can be instantiated with different hardware approaches and different reproduction mechanisms, but in all cases the system revolves around the conception of a new robot organism. The other components of the Triangle capture the principal stages of such a system; the Triangle as a whole serves as a guide for realizing this anticipated breakthrough and building systems where robot morphologies and controllers can evolve in real-time and real-space. After discussing this framework and the corresponding vision, we present a case study using the SYMBRION research project that realized some fragments of such a system in modular robot hardware.

Frontiers in Robotics and AI, 2015

Lecture Notes in Computer Science, 1998

This paper presents a comparative study of Evolutionary Algorithms (EAs) for Constraint Satisfact... more This paper presents a comparative study of Evolutionary Algorithms (EAs) for Constraint Satisfaction Problems (CSPs). We focus on EAs where fitness is based on penalization of constraint violations and the penalties are adapted during the execution. Three different EAs based on this approach are implemented. For highly connected constraint networks, the results provide further empirical support to the theoretical prediction of the phase transition in binary CSPs.