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To let biological processes, behaviors and structures inspire the design of problem solving algorithms and devices has been a prominent and persistent theme in engineering and applied sciences in the last few decades. Within this context, bio–inspired computing has taken a pioneering role. Fields such as evolutionary computing (1; 8; 25), artificial immune systems (4; 6; 43), membrane computing (29) or swarm systems (9; 22) have outgrown their infancy and found theoretical ground as well as important applications. The fact that and the way how these fields advanced into its current form is due to threemajor developments: (i) the upcoming of cheap, fast and reliable computational power in form of digital computers, (ii) the understanding that computational power in connectionwith implementing an algorithmic approach creates potent problem solvers, and (iii) the insight that biological systems can be fruitfully understood as information–processing units and can hence frequently be employed for computational and/or algorithmic proposes. This trend is of course not to be confused with computational biology, but it is highly related and probably unthinkable without the fundamental progress towards algorithmization and mathematization in biology, see e.g. (5; 16; 21; 38) for some recent discussion. Among the mentioned fields of bio–inspired computing, evolutionary algorithms and artificial immune systems play a unique role as their history is particularly long and the maturity reached is notably high. In this paper we will use both schemes in connection to solve the intertwined problem of maximum tracking and change detection in dynamic optimization.

Artificial Immune Systems, Dynamic Fitness Landscapes, and the Change Detection Problem