--Machine learning models have been found to be susceptible to adversarial examples that are often indistinguishable from the original inputs. These adversarial examples are created by applying adversarial perturbations to input samples, which would cause them to be misclassified by the target models. Attacks that search and apply the perturbations to create adversarial examples are performed in both white-box and black-box settings, depending on the information available to the attacker about the target. For black-box attacks, the only capability available to the attacker is the ability to query the target with specially crafted inputs and observing the labels returned by the model. Current black-box attacks either have low success rates, requires a high number of queries, or produce adversarial examples that are easily distinguishable from their sources. In this paper, we present AdversarialPSO, a black-box attack that uses fewer queries to create adversarial examples with high success rates. AdversarialPSO is based on the evolutionary search algorithm Particle Swarm Optimization, a population-based gradient-free optimization algorithm. It is flexible in balancing the number of queries submitted to the target vs the quality of imperceptible adversarial examples. The attack has been evaluated using the image classification benchmark datasets CIF AR-10, MNIST, and Imagenet, achieving success rates of 99.6%, 96.3%, and 82.0%, respectively, while submitting substantially fewer queries than the state-of-the-art. We also present a black-box method for isolating salient features used by models when making classifications. This method, called Swarms with Individual Search Spaces or SWISS, creates adversarial examples by finding and modifying the most important features in the input. The purpose of these two attacks is to help evaluate the robustness of machine learning models and to encourage the exploration of much-needed defenses. Deep learning (DL) is being used to solve a wide variety of problems in many different domains, such as image classification [1], malware detection [2], speech recognition [3], and medicine [4]. Despite state-of-the-art performances, DL models have been shown to suffer from a general flaw that makes them vulnerable to external attack. Adversaries can cause models to misclassify inputs by applying small perturbations to samples at test time [5].

What is a genetic algorithm? Bayesian inference ([1] links to particle methods in Bayesian statistics and hidden Markov chain models and [2] a tutorial on genetic particle models) Bioinformatics multiple sequence alignment.[1] SAGA is available on:.[4] Bioinformatics: Motif Discovery.[5] Calculation of bound states and local-density approximations. Code-breaking, using the GA to search large solution spaces of ciphers for the one correct decryption.[8]

Today we are joined by Gregg Willcox, Director of Research and Development at Unanimous AI. Starting out with a general interest in robotics, Gregg found himself in the world of machine learning and AI, inspired specifically by the idea of humans as smart data processors, instead of data points. With the team at Unanimous AI, Gregg uncovered a secret that many creatures in nature have been doing for centuries: using the collective intelligence of a group produces more accurate results, in a more efficient way, (also known as swarming), than an individual alone. From this research, 'Swarm' was born, a game-like collaboration platform that channels the beliefs and convictions of individuals to come to a consensus. Going one step further, using a behavioral neural network trained on people's behavior called'Conviction', the precision of the results is further amplified, leading to significant increases in detailed accuracy.

In the time series classification domain, shapelets are small time series that are discriminative for a certain class. It has been shown that classifiers are able to achieve state-of-the-art results on a plethora of datasets by taking as input distances from the input time series to different discriminative shapelets. Additionally, these shapelets can easily be visualized and thus possess an interpretable characteristic, making them very appealing in critical domains, such as the health care domain, where longitudinal data is ubiquitous. In this study, a new paradigm for shapelet discovery is proposed, which is based upon evolutionary computation. The advantages of the proposed approach are that (i) it is gradient-free, which could allow to escape from local optima more easily and to find suited candidates more easily and supports non-differentiable objectives, (ii) no brute-force search is required, which drastically reduces the computational complexity by several orders of magnitude, (iii) the total amount of shapelets and length of each of these shapelets are evolved jointly with the shapelets themselves, alleviating the need to specify this beforehand, (iv) entire sets are evaluated at once as opposed to single shapelets, which results in smaller final sets with less similar shapelets that result in similar predictive performances, and (v) discovered shapelets do not need to be a subsequence of the input time series. We present the results of experiments which validate the enumerated advantages.

Giuseppe Paolo 1, 2, Alban Laflaqui ere 2, Alexandre Coninx 1 and Stephane Doncieux 1 Abstract -- Performing Reinforcement Learning in sparse rewards settings, with very little prior knowledge, is a challenging problem since there is no signal to properly guide the learning process. In such situations, a good search strategy is fundamental. At the same time, not having to adapt the algorithm to every single problem is very desirable. Here we introduce T AXONS, a T ask Agnostic eXploration of Outcome spaces through Novelty and Surprise algorithm. Based on a population-based divergent-search approach, it learns a set of diverse policies directly from high-dimensional observations, without any task-specific information. T AXONS builds a repertoire of policies while training an autoencoder on the high-dimensional observation of the final state of the system to build a low-dimensional outcome space. The learned outcome space, combined with the reconstruction error, is used to drive the search for new policies. Results show that T AXONS can find a diverse set of controllers, covering a good part of the ground-truth outcome space, while having no information about such space.

Population-based memetic algorithms have been successfully applied to solve many difficult combinatorial problems. Often, a population of fixed size was used in such algorithms to record some best solutions sampled during the search. However, given the particular features of the problem instance under consideration, a population of variable size would be more suitable to ensure the best search performance possible. In this work, we propose variable population memetic search (VPMS), where a strategic population sizing mechanism is used to dynamically adjust the population size during the memetic search process. Our VPMS approach starts its search from a small population of only two solutions to focus on exploitation, and then adapts the population size according to the search status to continuously influence the balancing between exploitation and exploration. We illustrate an application of the VPMS approach to solve the challenging critical node problem (CNP). We show that the VPMS algorithm integrating a variable population, an effective local optimization procedure (called diversified late acceptance search) and a backbone-based crossover operator performs very well compared to state-of-the-art CNP algorithms. The algorithm is able to discover new upper bounds for 13 instances out of the 42 popular benchmark instances, while matching 23 previous best-known upper bounds.

Nature's spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongly from nonliving. It is, therefore, not surprising that this aspect of life should become a central focus of artificial life. We have known since Darwin that the diversity is produced dynamically, through the process of evolution; this has led life's creative productivity to be called Open-Ended Evolution (OEE) in the field. This article introduces the second of two special issues on current research in OEE and provides an overview of the contents of both special issues. Most of the work was presented at a workshop on open-ended evolution that was held as a part of the 2018 Conference on Artificial Life in Tokyo, and much of it had antecedents in two previous workshops on open-ended evolution at artificial life conferences in Cancun and York. We present a simplified categorization of OEE and summarize progress in the field as represented by the articles in this special issue.

The diversity of agent behaviors is an important topic for the quality of video games and virtual environments in general. Offering the most compelling experience for users with different skills is a difficult task, and usually needs important manual human effort for tuning existing code. This can get even harder when dealing with adaptive difficulty systems. Our paper's main purpose is to create a framework that can automatically create behaviors for game agents of different difficulty classes and enough diversity. In parallel with this, a second purpose is to create more automated tests for showing defects in the source code or possible logic exploits with less human effort.

Machine Learning (ML) is proving extremely beneficial in many healthcare applications. In pediatric oncology, retrospective studies that investigate the relationship between treatment and late adverse effects still rely on simple heuristics. To assess the effects of radiation therapy, treatment plans are typically simulated on phantoms, i.e., virtual surrogates of patient anatomy. Currently, phantoms are built according to reasonable, yet simple, human-designed criteria. This often results in a lack of individualization. We present a novel approach that combines imaging and ML to build individualized phantoms automatically. Given the features of a patient treated historically (only 2D radiographs available), and a database of 3D Computed Tomography (CT) imaging with organ segmentations and relative patient features, our approach uses ML to predict how to assemble a patient-specific phantom automatically. Experiments on 60 abdominal CTs of pediatric patients show that our approach constructs significantly more representative phantoms than using current phantom building criteria, in terms of location and shape of the abdomen and of two considered organs, the liver and the spleen. Among several ML algorithms considered, the Gene-pool Optimal Mixing Evolutionary Algorithm for Genetic Programming (GP-GOMEA) is found to deliver the best performing models, which are, moreover, transparent and interpretable mathematical expressions.

Transfer of Temporal Logic Formulas in Reinforcement Learning Zhe Xu and Ufuk Topcu Abstract Transferring high-level knowledge from a source task to a target task is an effective way to expedite reinforcement learning (RL). For example, propositional logic and first-order logic have been used as representations of such knowledge. We study the transfer of knowledge between tasks in which the timing of the events matters. We call such tasks temporal tasks . We concretize similarity between temporal tasks through a notion of logical transferability, and develop a transfer learning approach between different yet similar temporal tasks. We first propose an inference technique to extract metric interval temporal logic (MITL) formulas in sequential disjunctive normal form from labeled trajectories collected in RL of the two tasks. If logical transferability is identified through this inference, we construct a timed automaton for each sequential conjunctive subformula of the inferred MITL formulas from both tasks. We perform RL on the extended state which includes the locations and clock valuations of the timed automata for the source task. We then establish mappings between the corresponding components (clocks, locations, etc.) of the timed automata from the two tasks, and transfer the extended Q-functions based on the established mappings. Finally, we perform RL on the extended state for the target task, starting with the transferred extended Q-functions. Our results in two case studies show, depending on how similar the source task and the target task are, that the sampling efficiency for the target task can be improved by up to one order of magnitude by performing RL in the extended state space, and further improved by up to another order of magnitude using the transferred extended Q-functions. 1 Introduction Reinforcement learning (RL) has been successful in numerous applications.