Genetic recombination plays an important role in shaping the within-species genetic variations. It has been generally accepted that current-day population evolved from a small number of specific sequences called founders, and the genomic sequences (called recombinants) of individuals within the population are composed of segments from the founders due to recombination. A related computational p...

The adaptive critic heuristic has been a popular algorithm in reinforcement learning (RL) and approximate dynamic programming (ADP) alike. It is one of the first RL and ADP algorithms. RL and ADP algorithms are particularly useful for solving Markov decision processes (MDPs) that suffer from the curses of dimensionality and modeling. Many real-world problems however tend to be semi-Markov decis...

Answer Set Programming is a new paradigm based on logic programming. The main component of answer set programming is a system that finds the answer sets of logic programs. During the computation of an answer set, systems are faced with choice points where they have to select a literal and assign it a truth value. Generally, systems utilize some heuristics to choose new literals at the choice po...

Modeling of network-faults based time-sequence data by piecewise constant intensity function has been carried out using a heuristic approach that employs both Markov Chain Monte Carlo approach (MCMC) and Dynamic Programming algorithm (DPA) methodologies. The results for synthetic as well as for real data show that both MCMC and DPA have close agreement between predicted and actual values. Remar...

We examine the problem of how best to route jobs among two queues whose servers are subject to breakdowns and repairs. The optimal routing policy is computed by modelling the system as a discrete-time, finite-state Markov decision process and solving the resulting dynamic programming equations either iteratively or by applying the ‘policy improvement’ algorithm. In a series of numerical experim...

This correspondence describes an approach to reducing the computational cost of document image decoding by viewing it as a heuristic search problem. The kernel of the approach is a modi ed dynamic programming (DP) algorithm, called the iterated complete path (ICP) algorithm, that is intended for use with separable source models. A set of heuristic functions are presented for decoding formatted ...

In this paper the multi terminal q-FlowLoc problem (q-MT-FlowLoc) is introduced. FlowLoc problems combine two well-known modeling tools: (dynamic) network flows and locational analysis. Since the q-MT-FlowLoc problem is NP-hard we give a mixed integer programming formulation and propose a heuristic which obtains a feasible solution by calculating a maximum flow in a special graph H. If this flo...

We consider the approximate solution of discrete optimization problems using procedures that are capable of magnifying the effectiveness of any given heuristic algorithm through sequential application. In particular, we embed the problem within a dynamic programming framework, and we introduce several types of rollout algorithms, which are related to notions of policy iteration. We provide cond...