A very fast substring search algorithm
Communications of the ACM
Deconstructing the digit recognition problem
ML92 Proceedings of the ninth international workshop on Machine learning
Original Contribution: Stacked generalization
Neural Networks
Machine Learning
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Computability and complexity: from a programming perspective
Computability and complexity: from a programming perspective
The unknowable
Swarm intelligence
Blondie24: playing at the edge of AI
Blondie24: playing at the edge of AI
Evolutionary Computation: The Fossil Record
Evolutionary Computation: The Fossil Record
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The Undecidable: Basic Papers on Undecidable Propositions, Unsolvable Problems and Computable Functions
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Genetic Algorithms Reference
On the futility of blind search: An algorithmic view of “no free lunch”
Evolutionary Computation
Conservation of information in search: measuring the cost of success
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Evolutionary synthesis of nand logic: dissecting a digital organism
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
No free lunch and free leftovers theorems for multiobjective optimisation problems
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
Remarks on a recent paper on the "no free lunch" theorems
IEEE Transactions on Evolutionary Computation
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Hi-index | 0.00 |
Conservation of information (COI) popularized by the no free lunch theorem is a great leveler of search algorithms, showing that on average no search outperforms any other. Yet in practice some searches appear to outperform others. In consequence, some have questioned the significance of COI to the performance of search algorithms. An underlying foundation of COI is Bernoulli's Principle of Insufficient Reason(PrOIR) which imposes of a uniform distribution on a search space in the absence of all prior knowledge about the search target or the search space structure. The assumption is conserved under mapping. If the probability of finding a target in a search space is p, then the problem of finding the target in any subset of the search space is p. More generally, all some-to-many mappings of a uniform search space result in a new search space where the chance of doing better than p is 50-50. Consequently the chance of doing worse is 50-50. This result can be viewed as a confirming property of COI. To properly assess the significance of the COI for search, one must completely identify the precise sources of information that affect search performance. This discussion leads to resolution of the seeming conflict between COI and the observation that some search algorithms perform well on a large class of problems.