Symbolic regression (SR) is one the most popular applications of genetic programming (GP) and an attractive alternative to the standard deterministic regression approaches due to its flexibility in generating free-form mathematical models from observed data without any domain knowledge. However, GP suffers from various issues hindering the applicability of the technique to real-life problems. I...

We present a new connectionist framework for solving deterministic sequential decision problems based on temporal diierence learning and dynamic programming. The framework will be compared to Tesauro's approach of learning a strategy for playing backgammon, a proba-bilistic board game. It will be shown that his approach is not applicable for deterministic games, but simple modiications lead to ...

A hybrid of evolutionary programming (EP) and a deterministic optimization procedure is applied to a series of nonlinear and quadratic optimization problems. The hybrid scheme is compared with other existing schemes such as EP alone, two-phase (TP) optimization, and EP with a non-stationary Penalty function (NS-EP). The results indicate that the hybrid method can outperform the other methods wh...

The rollout algorithm is a suboptimal control method for deterministic and stochastic problems that can be solved by dynamic programming. In this short note, we derive an extension of the rollout algorithm that applies to constrained deterministic dynamic programming problems, and relies on a suboptimal policy, called base heuristic. Under suitable assumptions, we show that if the base heuristi...

Shortest path problem is a fundamental problem in network optimization and combinational optimization. The existing literature mainly concerned with the problem in deterministic, stochastic or fuzzy environments by using different tools. Different from the existing works, we investigate shortest path problem by regarding arc lengths as uncertain variables which are employed to describe the beha...

We consider a very general online scheduling problem with an objective to minimize the maximum level of resource allocated. We find a simple characterization of an optimal deterministic online algorithm. We develop further results for two more specific problems, single resource scheduling and hierarchical line balancing. We determine how to compute optimal online algorithms for both problems us...

When using today’s common shared-memory parallel programming models, subtle errors can lead to unintended nondeterministic behavior and bugs that appear only under certain thread interleavings. In contrast, we believe that a programming model should guarantee deterministic behavior unless the programmer specifically calls for nondeterminism. We describe our implementation of such a deterministi...

One obstacle to the implementation of modular extensions to programming languages lies in the problem of parsing extended languages. Specifically, the parse tables at the heart of traditional LALR(1) parsers are so monolithic and tightly constructed that, in the general case, it is impossible to extend them without regenerating them from the source grammar. Current extensible frameworks employ ...

We establish a duality theory for a broad class of deterministic inventory control problems on continuous spaces that includes the classical joint replenishment problem and inventory routing. Using this theory, we establish the existence of an optimal policy, which has been an open question. We show how a primal-dual pair of infinite dimensional linear programs encode both cyclic and non-cyclic...

We consider ranking and clustering problems related to the aggregation of inconsistent information. Ailon, Charikar, and Newman [1] proposed randomized constant factor approximation algorithms for these problems. Together with Hegde and Jain, we recently proposed deterministic versions of some of these randomized algorithms [2]. With one exception, these algorithms required the solution of a li...