This article introduces the Coincidence Algorithm (COIN) to solve several multimodal puzzles. COIN is an algorithm in the category of Estimation of Distribution Algorithms (EDAs) that makes use of probabilistic models to generate solutions. The model of COIN is a joint probability table of adjacent events (coincidence) derived from the population of candidate solutions. A unique characteristic of COIN is the ability to learn from a negative sample. Various experiments show that learning from a negative example helps to prevent premature convergence, promotes diversity and preserves good building blocks.

In this paper an algorithm based on Ant Colony Optimization techniques called Ants on a Tree (AOT) is introduced. This algorithm can integrate many algorithms together to solve a single problem. The strength of AOT is demonstrated by solving a High-Level Synthesis problem. A High-Level Synthesis problem consists of many design steps and many algorithms to solve each of them. AOT can easily integrate these algorithms to limit the search space and use them as heuristic weights to guide the search. During the search, AOT generates a dynamic decision tree. A boosting technique similar to branch and bound algorithms is applied to guide the search in the decision tree. The storage explosion problem is eliminated by the evaporation of pheromone trail generated by ants, the inherent property of our search algorithm.