Recently, a new class of heuristic techniques, the swarm intelligence has emerged. In this context, more recently, biologists and computer scientists in the ?eld of"arti?cial life"have been turning to insects for ideas that can be used for heuristics. Many aspects of the collective activities of social insects, such as foraging of ants, birds ?ocking and ?sh schooling are self-organizing, meaning that complex group behavior emerges from the interactions of in- viduals who exhibit simple behaviors by themselves. Swarm intelligence is an innovative computational way to solving hard problems. This discipline is mostly inspired by the behavior of ant colonies, bird ?ocks and ?sh schools and other biological creatures. In general, this is done by mimicking the behavior of these swarms. Swarm intelligence is an emerging research area with similar population and evolution characteristics to those of genetic algorithms. However, it di?erentiates in emphasizing the cooperative behavior among group m- bers. Swarm intelligence is used to solve optimization and cooperative pr- lems among intelligent agents, mainly in arti?cial network training, co- erative and/or decentralized control, operational research, power systems, electro-magnetics device design, mobile robotics, and others. The most we- knownrepresentativesofswarmintelligenceinoptimizationproblemsare:the food-searching behavior of ants, particle swarm optimization, and bacterial colonies. Real-world engineering problems often require concurrent optimization of several design objectives, which are con?icting in most of the cases. Such an optimization is generally called multi-objective or multi-criterion optimi- tion.Inthis context,the developmentofimprovementsfor swarmintelligence methods to multi-objective problems is an emergent research area.

Research on applying principles of quantum computing to improve the engineering of intelligent systems has been launched since late 1990s. This emergent research field concentrates on studying on quantum computing that is characterized by certain principles of quantum mechanics such as standing waves, interference, quantum bits, coherence, superposition of states, and concept of interference, combined with computational intelligence or soft computing approaches, such as artificial neural networks, fuzzy systems, evolutionary computing, swarm intelligence and hybrid soft computing methods. This volume offers a wide spectrum of research work developed using soft computing combined with quantum computing systems.

Researchers have obtained robots that display an amazing slew of behaviors and perform a multitude of tasks, including perception of environment, negotiating rough terrain, and pushing boxes. This volume offers a wide spectrum of sample works developed in leading research throughout the world about evolutionary mobile robotics and demonstrates the success of the technique in evolving efficient and capable mobile robots.

Recently, a new class of heuristic techniques, the swarm intelligence has emerged. In this context, more recently, biologists and computer scientists in the ?eld of"arti?cial life"have been turning to insects for ideas that can be used for heuristics. Many aspects of the collective activities of social insects, such as foraging of ants, birds ?ocking and ?sh schooling are self-organizing, meaning that complex group behavior emerges from the interactions of in- viduals who exhibit simple behaviors by themselves. Swarm intelligence is an innovative computational way to solving hard problems. This discipline is mostly inspired by the behavior of ant colonies, bird ?ocks and ?sh schools and other biological creatures. In general, this is done by mimicking the behavior of these swarms. Swarm intelligence is an emerging research area with similar population and evolution characteristics to those of genetic algorithms. However, it di?erentiates in emphasizing the cooperative behavior among group m- bers. Swarm intelligence is used to solve optimization and cooperative pr- lems among intelligent agents, mainly in arti?cial network training, co- erative and/or decentralized control, operational research, power systems, electro-magnetics device design, mobile robotics, and others. The most we- knownrepresentativesofswarmintelligenceinoptimizationproblemsare:the food-searching behavior of ants, particle swarm optimization, and bacterial colonies. Real-world engineering problems often require concurrent optimization of several design objectives, which are con?icting in most of the cases. Such an optimization is generally called multi-objective or multi-criterion optimi- tion.Inthis context,the developmentofimprovementsfor swarmintelligence methods to multi-objective problems is an emergent research area.

Research on applying principles of quantum computing to improve the engineering of intelligent systems has been launched since late 1990s. This emergent research field concentrates on studying on quantum computing that is characterized by certain principles of quantum mechanics such as standing waves, interference, quantum bits, coherence, superposition of states, and concept of interference, combined with computational intelligence or soft computing approaches, such as artificial neural networks, fuzzy systems, evolutionary computing, swarm intelligence and hybrid soft computing methods. This volume offers a wide spectrum of research work developed using soft computing combined with quantum computing systems.

Researchers have obtained robots that display an amazing slew of behaviors and perform a multitude of tasks, including perception of environment, negotiating rough terrain, and pushing boxes. This volume offers a wide spectrum of sample works developed in leading research throughout the world about evolutionary mobile robotics and demonstrates the success of the technique in evolving efficient and capable mobile robots.