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Almost all animals natural evolution has produced on Earth have a symmetrical body. In this paper we investigate the evolution of body symmetry in an artificial system where robots evolve. To this end, we define several measures to quantify symmetry in modular robots and see how these relate to fitness that corresponds to a locomotion task. We find that, although there is only a weak correlation between symmetry and fitness over the course of a single evolutionary run, there is a positive correlation between the level of symmetry and maximum fitness when a set of runs is taken into account.

Supplementary Material

We present a study on morphological traits of evolved modular robots. We note that the evolutionary search space –the set of obtainable morphologies– depends on the given representation and reproduction operators and we propose a framework to assess morphological traits in this search space regardless of a specific environment and/or task. To this end, we present eight quantifiable morphological descriptors and a generic novelty search algorithm to produce a diverse set of morphologies for any given representation. With this machinery, we perform a comparison between a direct encoding and a generative encoding. The results demonstrate that our framework permits to find a very diverse set of bodies, allowing a morphological diversity investigation. Furthermore, the analysis showed that despite the high levels of diversity, a bias to certain traits in the population was detected. Surprisingly, the two encoding methods showed no significant difference in the diversity levels of the evolved morphologies or their morphological traits.

Developing robotic systems that can evolve in real-time and real-space is a long term objective with technological as well as algorithmic milestones on the road. Technological prerequisites include advanced 3D-printing, automated assembly, and robust sensors and actuators. The necessary evolutionary mechanisms need not wait for these, they can be developed and investigated in simulations. In this paper, we present a system to simulate online evolution of constructible robots, where (1) the population members (robots) concurrently exist and evolve their morphologies and controllers, (2) all robots can be physically constructed. Experiments with this simulator provide us with insights into differences of using online and offline evolutionary setups.

International audience; In the context of social networks, maximizing influence means contacting the largest possible number of nodes starting from a set of seed nodes, and assuming a model for influence propagation. The real-world applications of influence maximization are of uttermost importance, and range from social studies to marketing campaigns. Building on a previous work on multi-objective evolutionary influence maximization, we propose improvements that not only speed up the optimization process considerably, but also deliver higher-quality results. State-of-the-art heuristics are run for different sizes of the seed sets, and the results are then used to initialize the population of a multi-objective evolutionary algorithm. The proposed approach is tested on three publicly available real-world networks, where we show that the evolutionary algorithm is able to improve upon the solutions found by the heuristics, while also converging faster than an evolutionary algorithm started from scratch.

We propose the Multi-strategy Differential Evolution (MsDE) algorithm to construct and maintain a self-adaptive ensemble of search strategies while solving an optimization problem. The ensemble of strategies is represented as agents that interact with the candidate solutions to improve their fitness. In the proposed algorithm, the performance of each agent is measured so that successful strategies are promoted within the ensemble. We propose two performance measures, and show their effectiveness in selecting successful strategies. We then present three population adaptation mechanisms, based on sampling, clone-best and clone-multiple adaptation schemes. The MsDE with different performance measures and population adaptation schemes is tested on the CEC2013 benchmark functions and compared with basic DE and with Self-Adaptive DE (SaDE). Our results show that MsDE is capable of efficiently adapting the strategies and parameters of DE and providing competitive results with respect to the state-of-the-art.

21st International Conference on Applications of Evolutionary Computation, EvoApplications 2018; parma; Italy; 4 April 2018 through 6 April 2018

Paul Kaufmann

This dissertation aims on analyzing fundamental concepts and dogmas of a graph-based genetic programming approach called Cartesian Genetic Programming (CGP) and introduces advanced genetic operators for CGP. The results of the experiments presented in this thesis lead to more knowledge about the algorithmic use of CGP and its underlying working mechanisms. CGP has been mostly used with a parametrization pattern, which has been prematurely generalized as the most efficient pattern for standard CGP and its variants. Several parametrization patterns are evaluated with more detailed and comprehensive experiments by using meta-optimization. This thesis also presents a first runtime analysis of CGP. The time complexity of a simple (1+1)-CGP algorithm is analyzed with a simple mathematical problem and a simple Boolean function problem. In the subfield of genetic operators for CGP, new recombination and mutation techniques that work on a phenotypic level are presented. The effectiveness of these operators is demonstrated on a widespread set of popular benchmark problems. Especially the role of recombination can be seen as a big open question in the field of CGP, since the lack of an effective recombination operator limits CGP to mutation-only use. Phenotypic exploration analysis is used to analyze the effects caused by the presented operators. This type of analysis also leads to new insights into the search behavior of CGP in continuous and discrete fitness spaces. Overall, the outcome of this thesis leads to a reconsideration of how CGP is effectively used and extends its adaption from Darwin's and Lamarck's theories of biological evolution.