Evolutionary Systems


The Pursuit of Creativity Can Make Algorithms Much Smarter

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In 2007, Kenneth Stanley, a computer scientist at the University of Central Florida, was playing with Picbreeder, a website he and his students had created, when an alien became a race car and changed his life. On Picbreeder, users would see an array of 15 similar images, composed of geometric shapes or swirly patterns, all variations on a theme. On occasion, some might resemble a real object, like a butterfly or a face. Users were asked to select one, and they typically clicked on whatever they found most interesting. Once they did, a new set of images, all variations on their choice, would populate the screen.


Overview of AI Libraries in Java Baeldung

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Since this article is about libraries, we'll not make any introduction to AI itself. Additionally, theoretical background of AI is necessary in order to use libraries presented in this article. AI is a very wide field, so we will be focusing on the most popular fields today like Natural Language Processing, Machine Learning, Neural Networks and more. In the end, we'll mention few interesting AI challenges where you can practice your understanding of AI. Apache Jena is an open source Java framework for building semantic web and linked data applications from RDF data.


Domain Generalization via Model-Agnostic Learning of Semantic Features

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Generalization capability to unseen domains is crucial for machine learning models when deploying to real-world conditions. We investigate the challenging problem of domain generalization, i.e., training a model on multi-domain source data such that it can directly generalize to target domains with unknown statistics. We adopt a model-agnostic learning paradigm with gradient-based meta-train and meta-test procedures to expose the optimization to domain shift. Further, we introduce two complementary losses which explicitly regularize the semantic structure of the feature space. Globally, we align a derived soft confusion matrix to preserve general knowledge about inter-class relationships.


D-Point Trigonometric Path Planning based on Q-Learning in Uncertain Environments

arXiv.org Artificial Intelligence

Finding the optimum path for a robot for moving from start to the goal position through obstacles is still a challenging issue. Thi s paper presents a novel path planning method, named D - point trigonometric, based on Q - learning algorithm for dynamic and uncertain environments, in which all the obstacles and the target are moving. We define a new state, action and reward functions for t he Q - learning by which the agent can find the best action in every state to reach the goal in the most appropriate path. Moreover, the experiment s in Unity3D confirmed the high convergence speed, the high hit rate, as well as the low dependency on environmental parameters of the proposed method compared with an opponent approach. The planning has been considered as a challenging concern in video games [1], transportation systems [2], and mobile robots [3] [4] . A s the most important path planning issues, w e can refer to the dynamics and the uncertainty of the environment, the smoothness and the length of the path, obstacle avoidance, and the computation al cost . In the last few decades, researchers have done numerous research efforts to present new approaches to solve them [5] [6] [7] [8] . Generally, most of the path planning approaches are categorized to one of the following methods [9] [10] [11]: ( 1) Classical methods (a) Computational geometry (CG) (b) Probabilistic r oadmap (PRM) (c) Potential fields method (PFM) ( 2) Heuristic and meta heuristic methods (a) Soft computing (b) Hybrid algorithms Since the complexity and the execution time of CG methods were high [11], PRMs were proposed to red uce the search space using techniques like milestones [12] .


Convex Optimisation for Inverse Kinematics

arXiv.org Machine Learning

W e consider the problem of inverse kinematics (IK), where one wants to find the parameters of a given kinematic skeleton that best explain a set of observed 3D joint locations. The kinematic skeleton has a tree structure, where each node is a joint that has an associated geometric transformation that is propagated to all its child nodes. The IK problem has various applications in vision and graphics, for example for tracking or reconstructing articulated objects, such as human hands or bodies. Most commonly, the IK problem is tackled using local optimisation methods. A major downside of these approaches is that, due to the non-convex nature of the problem, such methods are prone to converge to unwanted local optima and therefore require a good initialisation. In this paper we propose a convex optimisation approach for the IK problem based on semidef-inite programming, which admits a polynomial-time algorithm that globally solves (a relaxation of) the IK problem. Experimentally, we demonstrate that the proposed method significantly outperforms local optimisation methods using different real-world skeletons.


Artificial Intelligence May Better Detect Sleep Apnea - Docwire News

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Machine learning algorithms--also known as artificial intelligence (AI)--can better detect sleep apnea compared with traditional linear approaches, according to a study being presented at the CHEST Annual Meeting 2019. The researchers included 620 patients who were referred to a sleep lab in a suburban community sleep center. Researchers collected information on 12 select parameters: height, weight, waist, hip, body mass index, age, neck side, Modified Friedman stage, snoring, Epworth sleepiness scale, sex, and daytime sleepiness. During phase I, researchers used a binary particle swarm optimization technique to select the best sub-features that characterize sleep apnea. In phase II, they built an artificial neural network model based on a feedforward algorithm to detect sleep apnea.


GenSample: A Genetic Algorithm for Oversampling in Imbalanced Datasets

arXiv.org Machine Learning

Imbalanced datasets are ubiquitous. Classification performance on imbalanced datasets is generally poor for the minority class as the classifier cannot learn decision boundaries well. However, in sensitive applications like fraud detection, medical diagnosis, and spam identification, it is extremely important to classify the minority instances correctly. In this paper, we present a novel technique based on genetic algorithms, GenSample, for oversampling the minority class in imbalanced datasets. GenSample decides the rate of oversampling a minority example by taking into account the difficulty in learning that example, along with the performance improvement achieved by oversampling it. This technique terminates the oversampling process when the performance of the classifier begins to deteriorate. Consequently, it produces synthetic data only as long as a performance boost is obtained. The algorithm was tested on 9 real-world imbalanced datasets of varying sizes and imbalance ratios. It achieved the highest F-Score on 8 out of 9 datasets, confirming its ability to better handle imbalanced data compared to other existing methodologies.


Tractable Minor-free Generalization of Planar Zero-field Ising Models

arXiv.org Machine Learning

We present a new family of zero-field Ising models over $N$ binary variables/spins obtained by consecutive "gluing" of planar and $O(1)$-sized components and subsets of at most three vertices into a tree. The polynomial-time algorithm of the dynamic programming type for solving exact inference (computing partition function) and exact sampling (generating i.i.d. samples) consists in a sequential application of an efficient (for planar) or brute-force (for $O(1)$-sized) inference and sampling to the components as a black box. To illustrate the utility of the new family of tractable graphical models, we first build a polynomial algorithm for inference and sampling of zero-field Ising models over $K_{3,3}$-minor-free topologies and over $K_{5}$-minor-free topologies -- both are extensions of the planar zero-field Ising models -- which are neither genus - nor treewidth-bounded. Second, we demonstrate empirically an improvement in the approximation quality of the NP-hard problem of inference over the square-grid Ising model in a node-dependent non-zero "magnetic" field.


Diverse Behavior Is What Game AI Needs: Generating Varied Human-Like Playing Styles Using Evolutionary Multi-Objective Deep Reinforcement Learning

arXiv.org Machine Learning

Designing artificial intelligence for games (Game AI) has been long recognized as a notoriously challenging task in game industry, as it mainly relies on manual design, requiring plenty of domain knowledge. More frustratingly, even spending a lot of efforts, a satisfying Game AI is still hard to achieve by manual design due to the almost infinite search space. The recent success of deep reinforcement learning (DRL) sheds light on advancing automated game designing, significantly relaxing human competitive intelligent supp ort. However, existing DRL algorithms mostly focus on training a Game AI to win the game rather that the way it wins (style). To bridge the gap, we introduce EMO-DRL, an end-to-end game design framework, leveraging evolutionary algorithm, DRL and multi-objective optimization (MOO) to perform intelligent and automatic game design. Firstly, EMO-DRL proposes the style-oriented learning to bypass manual reward shaping in DRL and directly learns a Game AI with an expected style in an end-to-end fashion. On this basis, the prioritized multi-objective optimization is introduced to achieve more diverse, nature and humanlike Game AI. Large-scale evaluations on a Atari game and a commercial massively mul-tiplayer online game are conducted. The results demonstrat es that EMO-DRL, compared to existing algorithms, achieve better game designs in an intelligent and automatic way.


Solving dynamic multi-objective optimization problems via support vector machine

arXiv.org Artificial Intelligence

Dynamic Multi-objective Optimization Problems (DMOPs) refer to optimization problems that objective functions will change with time. Solving DMOPs implies that the Pareto Optimal Set (POS) at different moments can be accurately found, and this is a very difficult job due to the dynamics of the optimization problems. The POS that have been obtained in the past can help us to find the POS of the next time more quickly and accurately. Therefore, in this paper we present a Support Vector Machine (SVM) based Dynamic Multi-Objective Evolutionary optimization Algorithm, called SVM-DMOEA. The algorithm uses the POS that has been obtained to train a SVM and then take the trained SVM to classify the solutions of the dynamic optimization problem at the next moment, and thus it is able to generate an initial population which consists of different individuals recognized by the trained SVM. The initial populuation can be fed into any population based optimization algorithm, e.g., the Nondominated Sorting Genetic Algorithm II (NSGA-II), to get the POS at that moment. The experimental results show the validity of our proposed approach.