Wireless Sensor Networks (WSNs) are consist of independent distributed sensors with storing, processing, sensing and communication capabilities to monitor physical or environmental conditions. There are number of challenges in WSNs because of limitation of battery power, communications, computation and storage space. In the recent years, computational intelligence approaches such as evolutionar...

In India, water wastage in agricultural fields becomes a challenging issue and it is needed to minimize the loss of irrigation process. Since conventional system needs massive quantity utilization, smart can be designed with help recent technologies such as machine learning (ML) Internet Things (IoT). With this motivation, paper designs novel IoT enabled deep (IoTDL-SIS) technique. The goal IoT...

This paper describes Synthbee, an assistive tool for sound design which enables musicians to achieve desired sounds without managing parameters of a sound synthesizer manually. The system allows musicians to specify desired sound characteristics using attributes and explore the space of producible sounds by controlling the interactive evolutionary algorithm extended to take into account specifi...

Many real world problems can be modelled as an optimization problem. Evolutionary algorithms are used to solve these problems. Ant colony algorithm is a class of evolutionary algorithms that have been inspired of some specific ants looking for food in the nature. These ants leave trail pheromone on the ground to mark good ways that can be followed by other members of the group. Ant colony optim...

Evolution-in-materio (EIM) is a method that uses artificial evolution to exploit the properties of physical matter to solve computational problems without requiring a detailed understanding of such properties. EIM has so far been applied to very few computational problems. We show that using a purpose-built hardware platform called Mecobo, it is possible to evolve voltages and signals applied t...

Learning Classifier Systems use evolutionary algorithms to facilitate rule-discovery, where rule fitness is traditionally payoff based and assigned under a sharing scheme. Most current research has shifted to the use of accuracy-based fitness, after the introduction of XCS, where rule fitness is based on a rule's ability to predict the expected payoff from its use. Whilst XCS has been shown to ...

This paper illustrated an evolutionary algorithm which learns classifiers, represented as sets of fuzzy rules, from a data set containing past experimental observations of a phenomenon. The approach is applied to a benchmark dataset made available by the machine learning community.

Evolutionary robotics has been successful in creating agents that successfully link perception with appropriate action. However, the visual fields utilized by such agents is usually extremely small compared to the retinas linked to the visual cortex of animals. Evolving a cortex that processes larger fields of view in a selective and robust manner is challenging because fitness landscapes that ...

Multi–agent learning is a challenging open task in artificial intelligence. It is known an interesting connection between multi–agent learning algorithms and evolutionary game theory, showing that the learning dynamics of some algorithms can be modeled as replicator dynamics with a mutation term. Inspired by the recent sequence–form replicator dynamics, we develop a new version of theQ–learning...

We study the emergence of intelligent behavior within a simple intelligent agent. Cognitive agent functions are realized by mechanisms based on neural networks and evolutionary algorithms. The evolutionary algorithm is responsible for the adaptation of a neural network parameters based on the performance of the embodied agent endowed by different neural network architectures. In experiments, we...