EvoSys uses artificial intelligence (AI)-based technology to develop its final products. In particular, our area of expertise directly relates machine learning and computational intelligence (CI). The former is the science that attempts to extract knowledge from existing data using mathematics, while the latteris the science that attempts to understand and simulate intelligent behaviour through the modelling of natural intelligence, such as evolution, insects’ swarms, neural systems and immune systems. The main problems solved by CI paradigms include but are not limited to optimisation, classification, prediction and pattern recognition. EvoSys has achieved significant successes in solving real world problems using these techniques. Some examples of technologies used by EvoSys members include, but are not by any means limited to:
is one of the fastest growing areas of computer science. Based on Darwin’s theory of evolution, computer programs are measured against the task they are intended to do and then receive scores accordingly. The programs with the highest scores are considered the fittest. The fittest programs are then selected to join the evolutionary process via three standard genetic operators: crossover, mutation and reproduction. These operators aim to amend the program’s structure and create new offspring, which will be better. EvoSys has unique expertise in devising and designing appropriate representations, variation operators, and selection procedures to match the task at-hand, allowing us to customise the broad technology of evolutionary computation to find solutions for specific problems. This can offer an invaluable advantage.
are computer models loosely based on how the brain works by distributing knowledge across a network of neurons that are connected via adjustable weights. Such neural models can be extremely useful for pattern recognition, in that they can be trained to detect complex relationships among inputs, even when the statistical distributions of those inputs are unknown.EvoSys’ team often uses neural networks in combination with evolutionary computation to produce superior problem-solving software.
(PSO) is inspired by how birds flock and fish swarm. PSO is a computational method that optimises a problem by repeatedly trying to improve a candidate solution with regard to a given measure of quality. Thus, solutions fly in the parameter space following a leader. Our team often uses PSO in combination with evolutionary computation to produce superior problemsolving software.
a branch of artificial intelligence, concerns the construction and study of systems that can learn from data. For example, a machine learning system could be trained on email messages to learn to distinguish between spam and non-spam messages. After learning, it can then be used to classify new email messages into spam and non-spam folders.