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In Python 3.x the range() function [28] returns a generator which computes elements of the list on demand. Elements are only generated when they are needed (e.g., when print(r[3]) is evaluated in the following example), so this is an example of lazy or deferred evaluation: >>>
In a programming language, an evaluation strategy is a set of rules for evaluating expressions. [1] The term is often used to refer to the more specific notion of a parameter-passing strategy [2] that defines the kind of value that is passed to the function for each parameter (the binding strategy) [3] and whether to evaluate the parameters of a function call, and if so in what order (the ...
Evaluation of this symbol must yield the function for addition to make the example work as intended. Thus some dialects of Lisp allow an additional parameter for eval to specify the context of evaluation (similar to the optional arguments to Python's eval function - see below). An example in the Scheme dialect of Lisp (R 5 RS and later):
An example handcrafted evaluation function for chess might look like the following: c 1 * material + c 2 * mobility + c 3 * king safety + c 4 * center control + c 5 * pawn structure + c 6 * king tropism + ... Each of the terms is a weight multiplied by a difference factor: the value of white's material or positional terms minus black's.
The artificial landscapes presented herein for single-objective optimization problems are taken from Bäck, [1] Haupt et al. [2] and from Rody Oldenhuis software. [3] Given the number of problems (55 in total), just a few are presented here. The test functions used to evaluate the algorithms for MOP were taken from Deb, [4] Binh et al. [5] and ...
For example, the expression: print length([2+1, 3*2, 1/0, 5-4]) fails under strict evaluation because of the division by zero in the third element of the list. Under lazy evaluation, the length function returns the value 4 (i.e., the number of items in the list), since evaluating it does not attempt to evaluate the terms making up the list.
Theano is an open source project [3] primarily developed by the Montreal Institute for Learning Algorithms (MILA) at the Université de Montréal. [4]The name of the software references the ancient philosopher Theano, long associated with the development of the golden mean.
The next, "corrector" step refines the initial approximation by using the predicted value of the function and another method to interpolate that unknown function's value at the same subsequent point. Predictor–corrector methods for solving ODEs