A DYNAMIC PROGRAMMING ALGORITHM FOR OPTIMIZING BASEBALL STRATEGIES
نویسندگان
چکیده
منابع مشابه
An Approach towards Optimizing Random Forest using Dynamic Programming Algorithm
Random Forest (RF) is an ensemble supervised machine learning technique. Based on bagging and random feature selection, number of decision trees (base classifiers) is generated and majority voting is taken among them. The size of RF is subjective and varies from one dataset to another. Furthermore due to the randomization induced during creation, and its huge size, RF has at best been described...
متن کاملApproximate Dynamic Programming for Optimizing Oil Production
In this chapter, a new ADP algorithm integrating (1) systematic basis function construction, (2) a linear programming (LP) approach in DP, (3) adaptive basis function selection and (4) bootstrapping, is developed and applied to oil production problems. The procedure requires the solution of a large-scale dynamic system, which is accomplished using a subsurface flow simulator, for function evalu...
متن کاملOptimizing genetic algorithm strategies for evolving networks
This paper explores the use of genetic algorithms for the design of networks, where the demands on the network fluctuate in time. For varying network constraints, we find the best network using the standard genetic algorithm operators such as inversion, mutation and crossover. We also examine how the choice of genetic algorithm operators affects the quality of the best network found. Such netwo...
متن کاملA dynamic programming approach for solving nonlinear knapsack problems
Nonlinear Knapsack Problems (NKP) are the alternative formulation for the multiple-choice knapsack problems. A powerful approach for solving NKP is dynamic programming which may obtain the global op-timal solution even in the case of discrete solution space for these problems. Despite the power of this solu-tion approach, it computationally performs very slowly when the solution space of the pr...
متن کاملOptimizing Youth Baseball Batting Orders
Batting order (i.e., lineup) optimization for professional baseball teams has been analyzed for decades using various models and assumptions. In general, while optimization is useful – even with minimal benefit – it yields only small fractions of a run per game in improvement, mainly due to the interchangeability of most professional baseball players. Youth baseball, on the other hand, is a pri...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of the Operations Research Society of Japan
سال: 2019
ISSN: 0453-4514,2188-8299
DOI: 10.15807/jorsj.62.64