The purpose of this paper is to build a Regression Model for the Concrete Strengthening Process. The description of the process and the data set can be found in the following link: http://archive.ics.uci.edu/ml/datasets/Concrete Compressive Strength This is a free and a complex dataset available from the Machine Learning Repository of Centre of Machine Learning and Intelligent Systems at University of California Irvine Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients. These ingredients include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate and age.

We explore the use of Evolution Strategies (ES), a class of black box optimization algorithms, as an alternative to popular MDP-based RL techniques such as Q-learning and Policy Gradients. Experiments on MuJoCo and Atari show that ES is a viable solution strategy that scales extremely well with the number of CPUs available: By using a novel communication strategy based on common random numbers, our ES implementation only needs to communicate scalars, making it possible to scale to over a thousand parallel workers. This allows us to solve 3D humanoid walking in 10 minutes and obtain competitive results on most Atari games after one hour of training. In addition, we highlight several advantages of ES as a black box optimization technique: it is invariant to action frequency and delayed rewards, tolerant of extremely long horizons, and does not need temporal discounting or value function approximation.

Robotics and Automation deals with manufacture and applications of robots and computer systems for their control, sensory feedback, and information technology to reduce the need for human work. The journal provides an Open Access platform to publish the latest contributions in the field of robotics, automation technologies, robotic surgery, intelligent robotics, mechatronics, and biomimetics novel and biologically-inspired robotics, modelling, identification and control of robotic systems, biomedical, rehabilitation and surgical robotics, exoskeletons, prosthetics and artificial organs, AI, neural networks and fuzzy logic in robotics etc. This top best scholarly journal is using Editorial Manager System for online manuscript submission, review and tracking. Editorial board members of the Robotics & Automation or outside experts review manuscripts; at least two independent reviewer's approval followed by the editor is required for the acceptance of any citable manuscript. The journal includes a wide range of fields in its discipline to create a platform for the authors to make their contribution towards the journal and the editorial office promises a peer review process for the submitted manuscripts for the quality of publishing.

I was stuck up with a problem of multi class classification on which I am bound to use an evolutionary algorithm. As far as I know these are optimization algorithms. I could find out that genetic algorithms have been used to evolve a set of weights/rules for classification models. Is there a way in which I can involve EAs in the classification process?

In this article, I am going to explain the concept of genetic algorithm. First, I am going to present its origin and its goal. Then I am going to show you how to implement it with a short python tutorial. The naive solution is to create an "empirical algorithm" which is a set of rules: "if you meet this conditions, act like that". I could imagine that with enough rules like this we could reproduce natural intelligence.

As a researcher on Computer Vision, I come across new blogs and tutorials on ML (Machine Learning) every day. However, most of them are just focussing on introducing the syntax and the terminology relavant to the field. While people are able to copy paste and run the code in these tutorials and feel that working in ML is really not that hard, it doesn't help them at all in using ML for their own purposes. For example, they never introduce you to how you can run the same algorithm on your own dataset. Or, how do you get the dataset if you want to solve a problem.

This is part of a project I've been working in that involves using ML techniques to robot control. The first one is applying evolutionary algorithms to a neural controller. You cna find a Jupyter Notebook on the linked repository.

Fuzzy controllers are efficient and interpretable system controllers for continuous state and action spaces. To date, such controllers have been constructed manually or trained automatically either using expert-generated problem-specific cost functions or incorporating detailed knowledge about the optimal control strategy. Both requirements for automatic training processes are not found in most real-world reinforcement learning (RL) problems. In such applications, online learning is often prohibited for safety reasons because online learning requires exploration of the problem's dynamics during policy training. We introduce a fuzzy particle swarm reinforcement learning (FPSRL) approach that can construct fuzzy RL policies solely by training parameters on world models that simulate real system dynamics. These world models are created by employing an autonomous machine learning technique that uses previously generated transition samples of a real system. To the best of our knowledge, this approach is the first to relate self-organizing fuzzy controllers to model-based batch RL. Therefore, FPSRL is intended to solve problems in domains where online learning is prohibited, system dynamics are relatively easy to model from previously generated default policy transition samples, and it is expected that a relatively easily interpretable control policy exists. The efficiency of the proposed approach with problems from such domains is demonstrated using three standard RL benchmarks, i.e., mountain car, cart-pole balancing, and cart-pole swing-up. Our experimental results demonstrate high-performing, interpretable fuzzy policies.

Intelligent Systems Group Univ. of the Basque Country (UPV/EHU) San Sebastian, Spain Missing data has a ubiquitous presence in real-life applications of machine learning techniques. Imputation methods are algorithms conceived for restoring missing values in the data, based on other entries in the database. The choice of the imputation method has an influence on the performance of the machine learning technique, e.g., it influences the accuracy of the classification algorithm applied to the data. Therefore, selecting and applying the right imputation method is important and usually requires a substantial amount of human intervention. In this paper we propose the use of genetic programming techniques to search for the right combination of imputation and classification algorithms. We build our work on the recently introduced Python-based TPOT library, and incorporate a heterogeneous set of imputation algorithms as part of the machine learning pipeline search. We show that genetic programming can automatically find increasingly better pipelines that include the most effective combinations of imputation methods, feature preprocessing, and classifiers for a variety of classification problems with missing data.

While artificial intelligence (AI) has become widespread, many commercial AI systems are not yet accessible to individual researchers nor the general public due to the deep knowledge of the systems required to use them. We believe that AI has matured to the point where it should be an accessible technology for everyone. We present an ongoing project whose ultimate goal is to deliver an open source, user-friendly AI system that is specialized for machine learning analysis of complex data in the biomedical and health care domains. We discuss how genetic programming can aid in this endeavor, and highlight specific examples where genetic programming has automated machine learning analyses in previous projects.