Iaroslav Omelianenko

• Master of Engineering
• Research Director at NewGround

In the modern era, each Internet user leaves enormous amounts of auxiliary digital residuals (footprints) by using a variety of on-line services. All this data is already collected and stored for many years. In recent works, it was demonstrated that it's possible to apply simple machine learning methods to analyze collected digital footprints and t...

This study explores the use of coevolutionary methods to address the challenge of navigating through complex mazes using autonomous agents controlled by artificial neural networks (ANNs). It underscores a critical impediment to algorithmic optimization: the close interdependence between the task's goal and the objective function used for optimal so...

In this paper, we review the key features and major drawbacks of the NeuroEvolution of Augmenting Topologies (NEAT) algorithm, such as slow training speed that limits its area of application. The main reason for the performance issues of the NEAT algorithm is the huge number of calculations required at the end of each epoch to estimate the fitness...

The article deals with the problem of finding a solution for the navigational task of navigating a maze by an autonomous agent controlled by an artificial neural network (ANN). A solution to this problem was proposed by training the controlling ANN using the method of neuroevolution of augmenting topologies (NEAT). A description of the mathematical...

Cities were built to facilitate trade and protection. A smart city environment can be considered a product of the evolutionary process built around the coevolution of multiple autonomous artificial intelligent agents (AAIAs). The central concept of a genetic algorithm is a population of candidate solutions that evolve to solve optimisation problems...

This book will help you to apply popular neuroevolution strategies to existing neural network designs to improve their performance. It covers practical examples in areas such as games, robotics, and simulation of natural processes, using real-world examples and data sets for your better understanding.

The goal of the controller is to apply the force to the cart keeping two poles balanced as long as possible. At the same time, the cart should stay on track within the defined boundaries. As with single-pole balancing problem discussed before the control strategy can be defined as an avoidance control problem. Which means that the controller must m...

The single-pole balancer (a.k.a. inverted pendulum) is an unstable pendulum that has its center of mass above its pivot point. It can be stabilized by applying external forces under control of a specialized system that monitors the angle of the pole and moves the pivot point horizontally back and forth under the center of mass as it starts to fall....

The XOR problem solving is a classic computer science experiment in the field of reinforcement learning, which can not be solved without introducing non-linear execution to the solver algorithm. The two inputs to the XOR solver must be combined at some hidden unit, as opposed to only at the output node, because there is no function over a linear c...

In this paper, we look at how Artificial Swarm Intelligence can evolve using evolutionary algorithms that try to minimize the sensory surprise of the system. We will show how to apply the free-energy principle, borrowed from statistical physics, to quantitatively describe the optimization method (sensory surprise minimization), which can be used to...

In this paper, we look at how Artificial Swarm Intelligence can evolve using evolutionary algorithms that try to minimize the sensory surprise of the system. We will show how to apply the free-energy principle, borrowed from statistical physics, to quantitatively describe the optimization method (sensory surprise minimization), which can be used to...

Search for Novelty is an universal method of biological life evolution. We decided to apply it for Autonomous Artificial Intelligent Agents breeding using Neuro-Evolution algorithm to conduct evolutionary process.

This article presents how to build and train Artificial Neural Networks by NEAT algorithm. It will consider weakness of current Gradient Descent based training methods and shows a way to improve it. (Original article was published by author at Medium: https://medium.com/@io42/neuroevolution-evolving-artificial-neural-networks-topology-from-the-scra...

The NeuroEvolution (NE) is an artificial evolution of Neural Networks (NN) using genetic algorithms in order to find optimal NN parameters and topology. NeuroEvolution of NN may assume search for optimal weights of connections between NN nodes as well as search for optimal topology of resulting NN. The NEAT algorithm implemented in this work do se...

The presentation of research paper: Applying Deep Machine Learning for Psycho-Demographic Profiling of Internet Users using O.C.E.A.N. Model of Personality

In the modern era, each Internet user leaves enormous amounts of auxiliary digital residuals (footprints) by using a variety of on-line services. All this data is already collected and stored for many years. In recent works, it was demonstrated that it is possible to apply simple machine learning methods to analyze collected digital footprints and...

In this article we present the results of our research related to the study of correlations between specific visual stimulation and the elicited brain's electro-physiological response collected by EEG sensors from a group of participants. We will look at how the various characteristics of visual stimulation affect the measured electro-physiological...

Method for data exchange is characterized with preparation of files with information, advertisement or entertainment content suitable for loading to mobile device, with preparation of server of order of file distribution, loading files from server to computer connected to module of wireless data exchange, with placement of computer with module for...