Multi-sensor fusion: an Evolutionary algorithm approach

Modern decision-making processes rely on data coming from different sources. Intelligent integration and fusion of information from distributed multi-source, multi-sensor network requires an optimization-centered approach. Traditional optimization techniques often fail to meet the demands and challenges of highly dynamic and volatile information flow. New methods are required, which are capable of fully automated adjustment and self-adaptation to fluctuating inputs and tasks. One such method is Evolutionary algorithms (EA), a generic, flexible, and versatile framework for solving complex problems of global optimization and search in real world applications. The evolutionary approach provides a valuable alternative to traditional methods used in information fusion, due to its inherent parallel nature and its ability to deal with difficult problems. However, the application of the algorithm to a particular problem is often more an art than science. Choosing the right model and parameters requires an in-depth understanding of the morphological development of the algorithm, as well as its recent advances and trends. This paper attempts to give a compact overview of both basic and advanced concepts, models, and variants of Evolutionary algorithms in various implementations and applications particularly those in information fusion. We have brought together material scattered throughout numerous books, journal papers, and conference proceedings. Strong emphasis is made on the practical aspects of the EA implementation, including specific and detailed recommendations drawn from these various sources. However, the practical aspects are discussed from the standpoint of concepts and models, rather than from applications in specific problem domains, which emphasize the generality of the provided recommendations across different applications including information fusion.