The recent years have witnessed the rise of accurate but obscure decision systems which hide the logic of their internal decision processes to the users. The lack of explanations for the decisions of black box systems is a key ethical issue, and a limitation to the adoption of machine learning components in socially sensitive and safety-critical contexts. In this paper we focus on the problem of black box outcome explanation, i.e., explaining the reasons of the decision taken on a specific instance. We propose LORE, an agnostic method able to provide interpretable and faithful explanations. LORE first leans a local interpretable predictor on a synthetic neighborhood generated by a genetic algorithm. Then it derives from the logic of the local interpretable predictor a meaningful explanation consisting of: a decision rule, which explains the reasons of the decision; and a set of counterfactual rules, suggesting the changes in the instance's features that lead to a different outcome. Wide experiments show that LORE outperforms existing methods and baselines both in the quality of explanations and in the accuracy in mimicking the black box.

In this course, you will learn what hyperparameters are, what Genetic Algorithm is, and what hyperparameter optimization is. In this course, you will apply Genetic Algorithm to optimize the performance of Support Vector Machines and Multilayer Perceptron Neural Networks. Hyperparameter optimization will be done on two datasets, a regression dataset for the prediction of cooling and heating loads of buildings, and a classification dataset regarding the classification of emails into spam and non-spam. The SVM and MLP will be applied on the datasets without optimization and compare their results to after their optimization. By the end of this course, you will have learnt how to code Genetic Algorithm in Python and how to optimize your Machine Learning algorithms for maximal performance.

AI is arguably the biggest tech topic of 2018. From Google Duplex's human imitations and Spotify's song recommendations to Uber's self-driving cars and the Pentagon's use of GoogleAI, the technology seems to offer everything to everyone. You could say AI has become synonymous with progress via computing. However, not all AI is created equal, and for AI to fulfill its many promises, it needs to be creative. Let's start by addressing what I mean by "creative."

Soaring migrant birds exploit columns of rising air (thermals) to cover large distances with minimal energy. Using social information while locating thermals may benefit such birds, but examining collective movements in wild migrants has been a major challenge for researchers. We investigated the group movements of a flock of 27 naturally migrating juvenile white storks by using high-resolution GPS and accelerometers. Analyzing individual and group movements on multiple scales revealed that a small number of leaders navigated to and explored thermals, whereas followers benefited from their movements. Despite this benefit, followers often left thermals earlier and at lower height, and consequently they had to flap considerably more.

Extended Predictable Feature Analysis (PFAx) [Richthofer and Wiskott, 2017] is an extension of PFA [Richthofer and Wiskott, 2015] that allows generating a goal-directed control signal of an agent whose dynamics has previously been learned during a training phase in an unsupervised manner. PFAx hardly requires assumptions or prior knowledge of the agent's sensor or control mechanics, or of the environment. It selects features from a high-dimensional input by intrinsic predictability and organizes them into a reasonably low-dimensional model. While PFA obtains a well predictable model, PFAx yields a model ideally suited for manipulations with predictable outcome. This allows for goal-directed manipulation of an agent and thus for local navigation, i.e. for reaching states where intermediate actions can be chosen by a permanent descent of distance to the goal. The approach is limited when it comes to global navigation, e.g. involving obstacles or multiple rooms. In this article, we extend theoretical results from [Sprekeler and Wiskott, 2008], enabling PFAx to perform stable global navigation. So far, the most widely exploited characteristic of Slow Feature Analysis (SFA) was that slowness yields invariances. We focus on another fundamental characteristics of slow signals: They tend to yield monotonicity and one significant property of monotonicity is that local optimization is sufficient to find a global optimum. We present an SFA-based algorithm that structures an environment such that navigation tasks hierarchically decompose into subgoals. Each of these can be efficiently achieved by PFAx, yielding an overall global solution of the task. The algorithm needs to explore and process an environment only once and can then perform all sorts of navigation tasks efficiently. We support this algorithm by mathematical theory and apply it to different problems.

This course will guide you on what optimization is and what metaheuristics are. You will learn why we use metaheuristics in optimization problems as sometimes, when you have a complex problem you'd like to optimize, deterministic methods will not do; you will not be able to reach the best and optimal solution to your problem, therefore, metaheuristics should be used. This course covers information on metaheuristics and four widely used techniques which are Simulated Annealing, Genetic Algorithm, Tabu Search, and Evolutionary Strategies. By the end of this course, you will learn what Simulated Annealing, Genetic Algorithm, Tabu Search, and Evolutionary Strategies are, why they are used, how they work, and best of all, how to code them in Python! You will also learn how to handle constraints.

This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields. Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required.

This is the MATLAB code for the K-RVEA algorithm published in the following article: T. Chugh, Y. Jin, K. Miettinen, J. Hakanen, and K. Sindhya, A surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization, IEEE Transactions on Evolutionary Computation, vol. More details about it can be found in the thesis: T. Chugh. Please read the licence file before using the code and cite the article and the thesis if you use the code.

In this paper, we present a parallel genetic algorithm (pGA) with adaptive control parameters and permutation representation for weighted tardiness scheduling with sequence-dependent setups, an NP-Hard problem. This pGA provides a linear to slightly superlinear speedup relative to its sequential counterpart. As part of our research, we explore the effects of different random number generation algorithms on the runtimes of both sequential and parallel GAs. GAs and other forms of evolutionary computation rely so heavily on random number generation that our results show that we can obtain a 20% increase in the speed of a pGA, and an over 25% increase in the speed of a sequential GA, simply by careful choice of random number generator---both the underlying generator as well as algorithms for specific number types such as Gaussian often needed for mutating real-valued genes.

In this work, we describe a self-replication-based mechanism for designing agents of increasing complexity. We demonstrate the validity of this approach by solving simple, standard evolutionary computation problems in simulation. In the context of these simulation results, we describe the fundamental differences of this approach when compared to traditional approaches. Further, we highlight the possible advantages of applying this approach to the problem of designing complex artificial agents, along with the potential drawbacks and issues to be addressed in the future.