Vision-based decision-making tasks encompass a wide range of applications, including safety-critical domains where trustworthiness is as key as performance. These tasks are often addressed using Deep Reinforcement Learning (DRL) techniques, based on Artificial Neural Networks (ANNs), to automate sequential decision making. However, the "black-box" nature of ANNs limits their applicability in these settings, where transparency and accountability are essential. To address this, various explanation methods have been proposed; however, they often fall short in fully elucidating the decision-making pipeline of ANNs, a critical aspect for ensuring reliability in safety-critical applications. To bridge this gap, we propose an approach based on Graph-based Genetic Programming (GGP) to generate transparent policies for vision-based control tasks. Our evolved policies are constrained in size and composed of simple and well-understood operational modules, enabling inherent interpretability. We evaluate our method on three Atari games, comparing explanations derived from common explainability techniques to those derived from interpreting the agent's true computational graph. We demonstrate that interpretable policies offer a more complete view of the decision process than explainability methods, enabling a full comprehension of competitive game-playing policies.

Cartesian Genetic Programming (CGP) has previously shown capabilities in image processing tasks by evolving programs with a function set specialized for computer vision. A similar approach can be applied to Atari playing. Programs are evolved using mixed type CGP with a function set suited for matrix operations, including image processing, but allowing for controller behavior to emerge. While the programs are relatively small, many controllers are competitive with state of the art methods for the Atari benchmark set and require less training time. By evaluating the programs of the best evolved individuals, simple but effective strategies can be found.

Motivation: Segmental Duplications (SDs) are DNA fragments longer than 1 kbp, distributed within and between chromosomes and sharing more than 90% identity. Although they hold a significant role in genomic fluidity and adaptability, many key questions about their intrinsic characteristics and mutability remain unsolved due to the persistent difficulty of sequencing highly duplicated genomic regions. The recent development of long and linked-read NGS technologies will increase the need to search for SDs in genomes newly sequenced with these technics. The main limitation of SD analysis will soon be the availability of efficient detection software, to retrieve and compare SD genomic component between species or lineages. Results: In this paper, we present the open-source ASGART, 'A Segmental duplications Gathering And Refining Tool', developed to search for segmental duplications (SDs) in any assembled sequence. We have tested and benchmarked ASGART on five models organisms. Our results demonstrate ASGART's ability to extract SDs from any genome-wide sequence, regardless of genomic size or organizational complexity and quicker than any other software available. Availability and implementation: The online version of ASGART is available at http://asgart.irit.fr. The source code of ASGART is available both on the ASGART website and at https://github.com/delehef/asgart. Supplementary information: Supplementary data are available at Bioinformatics online.

Genetic Regulatory Networks (GRNs) implementations have a high degree of variability in their details. Parameters, encoding methods, and dynamics formulas all differ in the literature, and some GRN implementations have a high degree of model complexity. In this paper, we present a comparative study of different implementations of a GRN and introduce new variants for comparison. We use a modified Genetic Algorithm (GA) to evaluate GRN performance on a number of common benchmark tasks, with a focus on real-time control problems. We propose an encoding scheme and set of dynamics equations that simplifies implementation and evaluate the evolutionary fitness of this proposed method. Lastly, we use the comparative modifications study to demonstrate overall enhancements for GRN models.

We present DevoII, an improved cell-based developmental model for wind farm layout optimization. To address the shortcomings of discretization, DevoII's gene regulatory networks control cells that act in a continuous rather than discretized grid space. We find that DevoII is competitive, and in some cases, superior with respect to state-of-the-art global, stochastic search approaches when a suite of algorithms is evaluated on different wind scenarios. The modularity of the genetic regulatory network computational paradigm in terms of isolating its search algorithm, the regulatory network simulation and the cell simulation, allowed this improvement to largely focus upon cell simulation. This indicates a robustness property of the paradigm's design. As well, wflo highlights how developmental models can be considered more efficient than other optimization methods because of their "optimize once, use-many" adaptability.

In this paper, the strengths of the checkpoint-orientated modeling in synchronizing cell population are highlighted. Through different experiments, this work shows how to synchronize a population of asynchronous and heterogeneous cells with our proposed model of cell cycle. We will show that the probabilistic modeling undertaken accurately reproduces the dynamics of cell population under specific environmental conditions.

With the purpose of populating virtual worlds with various adapted artificial organisms, we propose an ontogenetic and phylogenetic hybrid model to generate complete organisms possessing metabolism, morphology, and behavior from a single initial cell. The initial purpose of our work is to generate organisms that are thereafter used to define complete organisms. In this paper, we introduce a bio-inspired cellular developmental model that links different approaches of ontogenesis systems: grammatical and cell chemistry approaches. Thus, we propose an alternative to parametric L-systems (APL-systems) in order to simulate morphogenesis of organisms according to their internal states. The developed organisms have a metabolism using environmental substrates to grow and to act. Moreover, they are able to exhibit almost perfect self-healing characteristics afterwards or even during their development.

Artificial embryogeny aims to develop a complete organism starting from a unique cell. For years, plenty of developmental models have been introduced. The main interests are reported on morphogen positioning, differentiation mechanisms and cellular interactions. In this paper, we show how the developmental model Cell2Organ has been extended to become a multi-level simulator able to work both on morphogen positioning thanks to an hydrodynamic layer and on cellular interaction with a physical layer. Through different experiments, we show the capacities of such a model with a “muscular joint” able to move in a physical world and a small organism able to create substrate vortices thanks to the hydrodynamic engine. The inspiration of such a set of simulators is provided the gastrulation stage of vertebrate embryos. During this stage, the embryo reorganizes its environment to continue its growth.

For living organisms, the robustness property is capital. For almost all of them, robustness rhymes with self-repairing. Indeed, organisms are subject to various injuries brought by the environment. To maintain their integrity, organisms are able to regenerate dead parts of themselves. This mechanism, commonly named self-repairing, is interesting to reproduce. Many works exist about self-repairing in robotics and electronics but fewer are in our domain of interest, artificial embryogenesis. In this paper, we show the self-repairing abilities of our model, Cell2Organ, designed to generate artificial creatures for artificial worlds. This model has previously been presented in.

Genetic algorithms are very demanding in terms of computing time and, when the population size is large, they need days to complete or even fail due to memory restrictions. It is particularly the case for artificial life where each evaluation can take more than one minute to develop an artificial creature, plant or organism. Indeed, creatures are developed in physical and chemical simulators that require important computation resources. In order to create more and more realistic creatures, we propose a grid parallelized version of genetic algorithms. Two possibilities exist to increase them: supercomputers or computational grids. Because of their scalability, we choose computational grid in their works.

In this article, we present our research on the evolution of morphology and behavior of complex creatures in virtual environments. We propose to study the evolution of creatures faced with different situations from crawling and walking to more complex activities like climbing and skating. Our creatures use solid 3D blocks and graphtals like Karl Sims' creatures and a new type of controller based on classifier system. The results constitute a new step toward creatures adapted to more complex environments

Classical chess engines exhaustively explore moving possibilities from a chessboard configuration to choose what the next best move to play is. In this article we present a new method to solve chess endgames without using Brute-Force algorithms or endgame tables. We are proposing to use Genetic Programming to combine elementary chess patterns defined by a chess expert. We apply this method specifically to the classical King-Rook-King endgame. We show that computed strategies are both effective and generic for they manage to win against several opponents (human players and artificial ones such as the chess engine CRAFTY). Besides, the method allows to propose strategies that are clearly readable and useable for a purpose such as teaching chess.

Animating entities in a virtual world is a complex problem. Many are solved using some scripting engines but programmers must spend a lot of time designing and implementing them. The use of learning engines tends to ease the work of the programmer. Learning classifiers systems (LCS) mix learning and evolution to generate adaptive behaviours. The extension of LCS to a polymorphic structure simplifies the rule coding without sacrificing performances. We present in this paper this generic structure and two applications of virtual reality using those systems to produce individual and group behaviours.

Prototype design and testing is an indispensable stage of any project development in many fields of activity, such as aeronautical, spatial, automotive industries or architecture. Scientists and engineers rely on prototyping for a visual confirmation and validation of both their ideas and concepts. The paper describes the design and implementation of PROVIS, a system for prototyping virtual systems in a collaborative way. The main goal of our works is to allow designers to replace physical mock-ups by virtual ones in order to test the integration and space requirements of the model components. To this end, PROVIS combines a "desktop VR" interface with an interactive 3D display. While discussing its software and hardware architecture, the paper describes how PROVIS allows users to interact with flexible or articulated objects, and to test maintenance procedures and component accessibility thanks to an original use of genetic algorithms.