The paper presents a "no free lunch" (NFL) theorem for search algorithms, demonstrating that all algorithms perform equally well on average over all possible cost functions. Specifically, if algorithm A outperforms B on some cost functions, then there must be an equal number of cost functions where B outperforms A. The authors analyze various characteristics of the search problem, such as its geometry and information-theoretic aspects, to derive mathematical benchmarks for assessing algorithm performance. They also investigate minimax aspects, the validity of using partial search results to predict future behavior, and time-varying cost functions. The paper concludes with a discussion on the justifiability of biologically-inspired search methods. The NFL theorem implies that no algorithm has an inherent advantage without knowledge of the cost function, highlighting the importance of understanding the specific characteristics of the problem at hand.The paper presents a "no free lunch" (NFL) theorem for search algorithms, demonstrating that all algorithms perform equally well on average over all possible cost functions. Specifically, if algorithm A outperforms B on some cost functions, then there must be an equal number of cost functions where B outperforms A. The authors analyze various characteristics of the search problem, such as its geometry and information-theoretic aspects, to derive mathematical benchmarks for assessing algorithm performance. They also investigate minimax aspects, the validity of using partial search results to predict future behavior, and time-varying cost functions. The paper concludes with a discussion on the justifiability of biologically-inspired search methods. The NFL theorem implies that no algorithm has an inherent advantage without knowledge of the cost function, highlighting the importance of understanding the specific characteristics of the problem at hand.