Self-adaptive exploration in evolutionary search

We address a primary question of computational as well as biological research on evolution: How can an exploration strategy adapt in such a way as to exploit the information gained about the problem at hand? We first introduce an integrated formalism of evolutionary search which provides a unified view on different specific approaches. On this basis we discuss the implications of indirect modeling (via a ``genotype-phenotype mapping'') on the exploration strategy. Notions such as modularity, pleiotropy and functional phenotypic complex are discussed as implications. Then, rigorously reflecting the notion of self-adaptability, we introduce a new definition that captures self-adaptability of exploration: different genotypes that map to the same phenotype may represent (also topologically) different exploration strategies; self-adaptability requires a variation of exploration strategies along such a ``neutral space''. By this definition, the concept of neutrality becomes a central concern of this paper. Finally, we present examples of these concepts: For a specific grammar-type encoding, we observe a large variability of exploration strategies for a fixed phenotype, and a self-adaptive drift towards short representations with highly structured exploration strategy that matches the ``problem's structure''.Comment: 24 pages, 5 figure

Optimal Parameter Choices Through Self-Adjustment: Applying the 1/5-th Rule in Discrete Settings

While evolutionary algorithms are known to be very successful for a broad range of applications, the algorithm designer is often left with many algorithmic choices, for example, the size of the population, the mutation rates, and the crossover rates of the algorithm. These parameters are known to have a crucial influence on the optimization time, and thus need to be chosen carefully, a task that often requires substantial efforts. Moreover, the optimal parameters can change during the optimization process. It is therefore of great interest to design mechanisms that dynamically choose best-possible parameters. An example for such an update mechanism is the one-fifth success rule for step-size adaption in evolutionary strategies. While in continuous domains this principle is well understood also from a mathematical point of view, no comparable theory is available for problems in discrete domains. In this work we show that the one-fifth success rule can be effective also in discrete settings. We regard the $(1+(\lambda,\lambda))$~GA proposed in [Doerr/Doerr/Ebel: From black-box complexity to designing new genetic algorithms, TCS 2015]. We prove that if its population size is chosen according to the one-fifth success rule then the expected optimization time on \textsc{OneMax} is linear. This is better than what \emph{any} static population size $\lambda$ can achieve and is asymptotically optimal also among all adaptive parameter choices.Comment: This is the full version of a paper that is to appear at GECCO 201

Learning to Control Differential Evolution Operators

Evolutionary algorithms are widely used for optimsation by researchers in academia and industry. These algorithms have parameters, which have proven to highly determine the performance of an algorithm. For many decades, researchers have focused on determining optimal parameter values for an algorithm. Each parameter configuration has a performance value attached to it that is used to determine a good configuration for an algorithm. Parameter values depend on the problem at hand and are known to be set in two ways, by means of offline and online selection. Offline tuning assumes that the performance value of a configuration remains same during all generations in a run whereas online tuning assumes that the performance value varies from one generation to another. This thesis presents various adaptive approaches each learning from a range of feedback received from the evolutionary algorithm. The contributions demonstrate the benefits of utilising online and offline learning together at different levels for a particular task. Offline selection has been utilised to tune the hyper-parameters of proposed adaptive methods that control the parameters of evolutionary algorithm on-the-fly. All the contributions have been presented to control the mutation strategies of the differential evolution. The first contribution demonstrates an adaptive method that is mapped as markov reward process. It aims to maximise the cumulative future reward. Next chapter unifies various adaptive methods from literature that can be utilised to replicate existing methods and test new ones. The hyper-parameters of methods in first two chapters are tuned by an offline configurator, irace. Last chapter proposes four methods utilising deep reinforcement learning model. To test the applicability of the adaptive approaches presented in the thesis, all methods are compared to various adaptive methods from literature, variants of differential evolution and other state-of-the-art algorithms on various single objective noiseless problems from benchmark set, BBOB

From evolutionary computation to the evolution of things

Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as evolutionary algorithms that take place in hardware are developed, opening up new avenues towards autonomous machines that can adapt to their environment. We discuss how evolutionary computation compares with natural evolution and what its benefits are relative to other computing approaches, and we introduce the emerging area of artificial evolution in physical systems

Digital Ecosystems: Ecosystem-Oriented Architectures

We view Digital Ecosystems to be the digital counterparts of biological ecosystems. Here, we are concerned with the creation of these Digital Ecosystems, exploiting the self-organising properties of biological ecosystems to evolve high-level software applications. Therefore, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. The Digital Ecosystem was then measured experimentally through simulations, with measures originating from theoretical ecology, evaluating its likeness to biological ecosystems. This included its responsiveness to requests for applications from the user base, as a measure of the ecological succession (ecosystem maturity). Overall, we have advanced the understanding of Digital Ecosystems, creating Ecosystem-Oriented Architectures where the word ecosystem is more than just a metaphor.Comment: 39 pages, 26 figures, journa

JavaEvA : a Java based framework for Evolutionary Algorithms

Das Softwarepaket JavaEvA (eine Java Implementierung Evolutionärer Algorithmen) ist ein allgemeines modulares Framework für Optimierungsalgorithmen basierend auf einer Client-Server Architektur, das geeignet ist eine Vielzahl von Optimierungsproblemen zu lösen. Das Paket wurde mit dem Schwerpunkt entwickelt neue Verfahren im Bereich der Evolutionären Algorithmen einfach entwickeln und testen zu können und diese Verfahren letztlich in praktischen Anwendungen anzuwenden. JavaEvA beinhaltet Implementierungen der üblichen Evolutionären Verfahren wie zum Beispiel Genetische Algorithmen, die CHC Adaptive Search, Population Based Incremental Learning, Evolutionsstrategien, Modellunterstützte Evolutionsstrategien, Genetisches Programmieren und Grammatical Evolution. Zusätzlich erlaubt es das modulare Framework von JavaEvA eigene eventuell problemspezifische Optimierungsmodule zu ergänzen und mit den implementieren Verfahren zu vergleichen. Das JavaEvA Paket benutzt ein generisches Verfahren zur GUI Generierung und erlaubt so einen einfachen Objektorientierten Zugang zu allen relevanten Parametern eines Evolutionären Algorithmus. Das gleiche Verfahren generiert auch entsprechende GUI Elemente für neu entwickelte Methoden und vereinfacht so den Aufwand bei der Entwicklung neuer Methoden erheblich. Zusätzlich besteht die Möglichkeit spezialisiere GUI Elemente für einzelne Objekte zu entwickeln und in das bestehende System zu integrieren, um die Benutzerfreundlichkeit weiter zu erhöhen. Da es uns unmöglich ist jedwede potenzielle Anwendung oder Optimierungsproblem zu antizipieren, ist es aus praktischen Gründen fast immer nötig eigene Implementierungen des jeweiligen Anwendungsproblems zu erstellen. Um diesen Vorgang zu erleichtern bietet diese Anleitung zusätzliche Beispiele mit detaillierten Beschreibungen, wie man ein eigenes Problem implementieren kann und JavaEvA lediglich als Optimierungstoolbox integriert. Auf diese Weise behält ihre jeweilige Anwendung die vollständige Kontrolle über die verwendeten Verfahren und die anwendungsspezifische Darstellung der Optimierungsergebnisse.The package JavaEvA (a Java implementation of Evolutionary Algorithms) is a general modular framework with an inherent client server structure to solve practical optimization problems. This package was especially designed to test and develop new approaches for Evolutionary Algorithms and to utilize them in real-world applications. JavaEvA already provides implementations of the most common Evolutionary Algorithms, like Genetic Algorithms, CHC Adaptive Search, Population Based Incremental Learning, Evolution Strategies, Model-Assisted Evolution Strategies, Genetic Programming and Grammatical Evolution. In addition the modular framework of JavaEvA allows everyone to add their own optimization modules to meet their specific requirements. The JavaEvA package uses a generic GUI framework that allows GUI access to any member of a class if get and set methods are provided and an editor is defined for the given data type. This approach allows very fast development cycles, since hardly any additional effort is necessary for implementing GUI elements, while still at the same time user specific GUI elements can be developed and integrated to increase usability. Since we cannot anticipate specific optimization problem and requirements, it is necessary for users to define their optimization problem. Therefore, we provide an additional framework and explain how one can include JavaEvA in an existing Java project or how one can implement ones own optimization problem and optimize it by using JavaEvA. This gives users total control of the optimization algorithms used