Evolutionary Multi-objective Optimization in Uncertain Environments

Name: Evolutionary Multi-objective Optimization in Uncertain Environments
Brand: Springer Nature
SKU: 9783540959762
Availability: InStock

Best Price:
Buy Evolutionary Multi-objective Optimization in Uncertain Environments for $84.99 at @ Link.springer.com
No coupon is required — this is the standard retail price.

Set a price drop alert to never miss an offer.

1 Offer Price Range: $84.99 - $84.99

BEST PRICE

Single Product Purchase

$84.99

@ Link.springer.com BUY Now

Price Comparison

Seller	Contact Seller	List Price	On Sale	Shipping	Best Promo	Final Price	Volume Discount	Financing	Availability	Seller's Page
BEST PRICE 1 Product Purchase @ Link.springer.com		$84.99	$84.99			$84.99		See Site	In stock	Visit Store

Product Details

Brand

Springer Nature

Manufacturer

N/A

Part Number

GTIN

9783540959762

Condition

New

Product Description

Evolutionary algorithms are sophisticated search methods that have been found to be very efficient and effective in solving complex real-world multi-objective problems where conventional optimization tools fail to work well. Despite the tremendous amount of work done in the development of these algorithms in the past decade, many researchers assume that the optimization problems are deterministic and uncertainties are rarely examined. The primary motivation of this book is to provide a comprehensive introduction on the design and application of evolutionary algorithms for multi-objective optimization in the presence of uncertainties. In this book, we hope to expose the readers to a range of optimization issues and concepts, and to encourage a greater degree of appreciation of evolutionary computation techniques and the exploration of new ideas that can better handle uncertainties. "Evolutionary Multi-Objective Optimization in Uncertain Environments: Issues and Algorithms" is intended for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of evolutionary multi-objective optimization and uncertainties.

Available Colors

N/A

Available Sizes

N/A