Evolvability signatures of generative encodings: beyond standard performance benchmarks

Evolutionary robotics is a promising approach to autonomously synthesize machines with abilities that resemble those of animals, but the field suffers from a lack of strong foundations. In particular, evolutionary systems are currently assessed solely by the fitness score their evolved artifacts can achieve for a specific task, whereas such fitness-based comparisons provide limited insights about how the same system would evaluate on different tasks, and its adaptive capabilities to respond to changes in fitness (e.g., from damages to the machine, or in new situations). To counter these limitations, we introduce the concept of "evolvability signatures", which picture the post-mutation statistical distribution of both behavior diversity (how different are the robot behaviors after a mutation?) and fitness values (how different is the fitness after a mutation?). We tested the relevance of this concept by evolving controllers for hexapod robot locomotion using five different genotype-to-phenotype mappings (direct encoding, generative encoding of open-loop and closed-loop central pattern generators, generative encoding of neural networks, and single-unit pattern generators (SUPG)). We observed a predictive relationship between the evolvability signature of each encoding and the number of generations required by hexapods to adapt from incurred damages. Our study also reveals that, across the five investigated encodings, the SUPG scheme achieved the best evolvability signature, and was always foremost in recovering an effective gait following robot damages. Overall, our evolvability signatures neatly complement existing task-performance benchmarks, and pave the way for stronger foundations for research in evolutionary robotics.Comment: 24 pages with 12 figures in the main text, and 4 supplementary figures. Accepted at Information Sciences journal (in press). Supplemental videos are available online at, see http://goo.gl/uyY1R

Improving Scalability of Evolutionary Robotics with Reformulation

Creating systems that can operate autonomously in complex environments is a challenge for contemporary engineering techniques. Automatic design methods offer a promising alternative, but so far they have not been able to produce agents that outperform manual designs. One such method is evolutionary robotics. It has been shown to be a robust and versatile tool for designing robots to perform simple tasks, but more challenging tasks at present remain out of reach of the method. In this thesis I discuss and attack some problems underlying the scalability issues associated with the method. I present a new technique for evolving modular networks. I show that the performance of modularity-biased evolution depends heavily on the morphology of the robot’s body and present a new method for co-evolving morphology and modular control. To be able to reason about the new technique I develop reformulation framework: a general way to describe and reason about metaoptimization approaches. Within this framework I describe a new heuristic for developing metaoptimization approaches that is based on the technique for co-evolving morphology and modularity. I validate the framework by applying it to a practical task of zero-g autonomous assembly of structures with a fleet of small robots. Although this work focuses on the evolutionary robotics, methods and approaches developed within it can be applied to optimization problems in any domain

Simulation Intelligence: Towards a New Generation of Scientific Methods

The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence operating system stack (SI-stack) and the motifs therein: (1) Multi-physics and multi-scale modeling; (2) Surrogate modeling and emulation; (3) Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based modeling; (6) Probabilistic programming; (7) Differentiable programming; (8) Open-ended optimization; (9) Machine programming. We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science

Técnicas de optimización paralelas : esquema híbrido basado en hiperheurísticas y computación evolutiva

Optimisation is the process of selecting the best element fr om a set of available alternatives. Solutions are termed good or bad depending on its performance for a set of objectives. Several algorithms to deal with such kind of problems have been defined in the literature. Metaheuristics are one of the most prominent techniques. They are a class of modern heuristics whose main goal is to com bine heuristics in a problem independent way with the aim of improving their per formance. Meta- heuristics have reported high-quality solutions in severa l fields. One of the reasons of the good behaviour of metaheuristics is that they are defin ed in general terms. Therefore, metaheuristic algorithms can be adapted to fit th e needs of most real-life optimisation. However, such an adaptation is a hard task, and it requires a high computational and user effort. There are two main ways of reducing the effort associated to th e usage of meta- heuristics. First, the application of hyperheuristics and parameter setting strategies facilitates the process of tackling novel optimisation pro blems and instances. A hyperheuristic can be viewed as a heuristic that iterativel y chooses between a set of given low-level metaheuristics in order to solve an optim isation problem. By using hyperheuristics, metaheuristic practitioners do no t need to manually test a large number of metaheuristics and parameterisations for d iscovering the proper algorithms to use. Instead, they can define the set of configur ations which must be tested, and the model tries to automatically detect the be st-behaved ones, in order to grant more resources to them. Second, the usage of pa rallel environments might speedup the process of automatic testing, so high qual ity solutions might be achieved in less time. This research focuses on the design of novel hyperheuristic s and defines a set of models to allow their usage in parallel environments. Differ ent hyperheuristics for controlling mono-objective and multi-objective multi-po int optimisation strategies have been defined. Moreover, a set of novel multiobjectivisa tion techniques has been proposed. In addition, with the aim of facilitating the usage of multiobjectivi- sation, the performance of models that combine the usage of m ultiobjectivisation and hyperheuristics has been studied. The proper performance of the proposed techniques has been v alidated with a set of well-known benchmark optimisation problems. In addi tion, several practical and complex optimisation problems have been addressed. Som e of the analysed problems arise in the communication field. In addition, a pac king problem proposed in a competition has been faced up. The proposals for such pro blems have not been limited to use the problem-independent schemes. Inste ad, new metaheuristics, operators and local search strategies have been defined. Suc h schemes have been integrated with the designed parallel hyperheuristics wit h the aim of accelerating the achievement of high quality solutions, and with the aim of fa cilitating their usage. In several complex optimisation problems, the current best -known solutions have been found with the methods defined in this dissertation.Los problemas de optimización son aquellos en los que hay que elegir cuál es la solución más adecuada entre un conjunto de alternativas. Actualmente existe una gran cantidad de algoritmos que permiten abordar este tipo de problemas. Entre ellos, las metaheurísticas son una de las técnicas más usadas. El uso de metaheurísticas ha posibilitado la resolución de una gran cantidad de problemas en diferentes campos. Esto se debe a que las metaheurísticas son técnicas generales, con lo que disponen de una gran cantidad de elementos o parámetros que pueden ser adaptados a la hora de afrontar diferentes problemas de optimización. Sin embargo, la elección de dichos parámetros no es sencilla, por lo que generalmente se requiere un gran esfuerzo computacional, y un gran esfuerzo por parte del usuario de estas técnicas. Existen diversas técnicas que atenúan este inconveniente. Por un lado, existen varios mecanismos que permiten seleccionar los valores de dichos parámetros de forma automática. Las técnicas más simples utilizan valores fijos durante toda la ejecución, mientras que las técnicas más avanzadas, como las hiperheurísticas, adaptan los valores usados a las necesidades de cada fase de optimización. Además, estas técnicas permiten usar varias metaheurísticas de forma simultánea. Por otro lado, el uso de técnicas paralelas permite acelerar el proceso de testeo automático, reduciendo el tiempo necesario para obtener soluciones de alta calidad. El objetivo principal de esta tesis ha sido diseñar nuevas hiperheurísticas e integrarlas en el modelo paralelo basado en islas. Estas técnicas se han usado para controlar los parámetros de varias metaheurísticas evolutivas. Se han definido diversas hiperheurísticas que han permitido abordar tanto problemas mono-objetivo como problemas multi-objetivo. Además, se han definido un conjunto de multiobjetivizaciones, que a su vez se han beneficiado de las hiperheurísticas propuestas. Las técnicas diseñadas se han validado con algunos de los problemas de test más ampliamente utilizados. Además, se han abordado un conjunto de problemas de optimización prácticos. Concretamente, se han tratado tres problemas que surgen en el ámbito de las telecomunicaciones, y un problema de empaquetado. En dichos problemas, además de usar las hiperheurísticas y multiobjetivizaciones, se han definido nuevos algoritmos, operadores, y estrategias de búsqueda local. En varios de los problemas, el uso combinado de todas estas técnicas ha posibilitado obtener las mejores soluciones encontradas hasta el momento

Z-Numbers-Based Approach to Hotel Service Quality Assessment

In this study, we are analyzing the possibility of using Z-numbers for measuring the service quality and decision-making for quality improvement in the hotel industry. Techniques used for these purposes are based on consumer evalu- ations - expectations and perceptions. As a rule, these evaluations are expressed in crisp numbers (Likert scale) or fuzzy estimates. However, descriptions of the respondent opinions based on crisp or fuzzy numbers formalism not in all cases are relevant. The existing methods do not take into account the degree of con- fidence of respondents in their assessments. A fuzzy approach better describes the uncertainties associated with human perceptions and expectations. Linguis- tic values are more acceptable than crisp numbers. To consider the subjective natures of both service quality estimates and confidence degree in them, the two- component Z-numbers Z = (A, B) were used. Z-numbers express more adequately the opinion of consumers. The proposed and computationally efficient approach (Z-SERVQUAL, Z-IPA) allows to determine the quality of services and iden- tify the factors that required improvement and the areas for further development. The suggested method was applied to evaluate the service quality in small and medium-sized hotels in Turkey and Azerbaijan, illustrated by the example

Novel approaches to cooperative coevolution of heterogeneous multiagent systems

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2017Heterogeneous multirobot systems are characterised by the morphological and/or behavioural heterogeneity of their constituent robots. These systems have a number of advantages over the more common homogeneous multirobot systems: they can leverage specialisation for increased efficiency, and they can solve tasks that are beyond the reach of any single type of robot, by combining the capabilities of different robots. Manually designing control for heterogeneous systems is a challenging endeavour, since the desired system behaviour has to be decomposed into behavioural rules for the individual robots, in such a way that the team as a whole cooperates and takes advantage of specialisation. Evolutionary robotics is a promising alternative that can be used to automate the synthesis of controllers for multirobot systems, but so far, research in the field has been mostly focused on homogeneous systems, such as swarm robotics systems. Cooperative coevolutionary algorithms (CCEAs) are a type of evolutionary algorithm that facilitate the evolution of control for heterogeneous systems, by working over a decomposition of the problem. In a typical CCEA application, each agent evolves in a separate population, with the evaluation of each agent depending on the cooperation with agents from the other coevolving populations. A CCEA is thus capable of projecting the large search space into multiple smaller, and more manageable, search spaces. Unfortunately, the use of cooperative coevolutionary algorithms is associated with a number of challenges. Previous works have shown that CCEAs are not necessarily attracted to the global optimum, but often converge to mediocre stable states; they can be inefficient when applied to large teams; and they have not yet been demonstrated in real robotic systems, nor in morphologically heterogeneous multirobot systems. In this thesis, we propose novel methods for overcoming the fundamental challenges in cooperative coevolutionary algorithms mentioned above, and study them in multirobot domains: we propose novelty-driven cooperative coevolution, in which premature convergence is avoided by encouraging behavioural novelty; and we propose Hyb-CCEA, an extension of CCEAs that places the team heterogeneity under evolutionary control, significantly improving its scalability with respect to the team size. These two approaches have in common that they take into account the exploration of the behaviour space by the evolutionary process. Besides relying on the fitness function for the evaluation of the candidate solutions, the evolutionary process analyses the behaviour of the evolving agents to improve the effectiveness of the evolutionary search. The ultimate goal of our research is to achieve general methods that can effectively synthesise controllers for heterogeneous multirobot systems, and therefore help to realise the full potential of this type of systems. To this end, we demonstrate the proposed approaches in a variety of multirobot domains used in previous works, and we study the application of CCEAs to new robotics domains, including a morphological heterogeneous system and a real robotic system.Fundação para a Ciência e a Tecnologia (FCT, PEst-OE/EEI/LA0008/2011

Rapid and Thorough Exploration of Low Dimensional Phenotypic Landscapes

PhDThis thesis presents two novel algorithms for the evolutionary optimisation of agent populations through divergent search of low dimensional phenotypic landscapes. As the eld of Evolutionary Robotics (ER) develops towards more complex domains, which often involve deception and uncertainty, the promotion of phenotypic diversity has become of increasing interest. Divergent exploration of the phenotypic feature space has been shown to avoid convergence towards local optima and to provide diverse sets of solutions to a given objective. Novelty Search (NS) and the more recent Multi-dimensional Archive of Phenotypic Elites (MAP-Elites), are two state of the art algorithms which utilise divergent phenotypic search. In this thesis, the individual merits and weaknesses of these algorithms are built upon in order to further develop the study of divergent phenotypic search within ER. An observation that the diverse range of individuals produced through the optimisation of novelty will likely contain solutions to multiple independent objectives is utilised to develop Multiple Assessment Directed Novelty Search (MADNS). The MADNS algorithm is introduced as an extension to NS for the simultaneous optimisation of multiple independent objectives, and is shown to become more e ective than NS as the size of the state space increases. The central contribution of this thesis is the introduction of a novel algorithm for rapid and thorough divergent search of low dimensional phenotypic landscapes. The Spatial, Hierarchical, Illuminated NeuroEvolution (SHINE) algorithm di ers from previous divergent search algorithms, in that it utilises a tree structure for the maintenance and selection of potential candidates. Unlike previous approaches, SHINE iteratively focusses upon sparsely visited areas of the phenotypic landscape without the computationally expensive distance comparison required by NS; rather, the sparseness of the area within the landscape where a potential solution resides is inferred through its depth within the tree. Experimental results in a range of domains show that SHINE signi cantly outperforms NS and MAP-Elites in both performance and exploration