Old and new views of Niagara Falls : with short description /

On cover: Niagara.

Second Welland Canal - Book 3, Survey Map 5 - Old Canal and New Canal along Chippewa Creek in Port Robinson, Thorold Township

Survey map of the Second Welland Canal created by the Welland Canal Company showing the canal along Chippewa Creek in Thorold Township. Identified structures and features associated with the Canal include the towing path, float bridge, and the waterway itself. The surveyors' measurements and notes can be seen in red and black ink and pencil. Local area landmarks are also identified and include a road allowance between Lot 213 and 214, Chippewa Creek, an unnamed creek, and the Old Canal. Wetlands adjacent to Chippewa Creek are illustrated. Properties and property owners of note are: Lots 213 and 214, Samuel Hill, and Duncan Coleman. The boundary of the land deeded to Coleman is outlined in blue.

Letter to H.H. Collier of Austin, Texas to the care of Cruger and Moore of Houston, Texas and New Orleans

Letter to H.H. Collier of Austin, Texas to the care of Cruger and Moore of Houston, Texas and New Orleans. The letter is from his sister, E. Richards. She writes about family life, her job as a teacher and politics (3 ¼ pages, handwritten), Jan. 23, 1841.

A Scalability Study and New Algorithms for Large-Scale Many-Objective Optimization

Many real-world optimization problems contain multiple (often conflicting) goals to be optimized concurrently, commonly referred to as multi-objective problems (MOPs). Over the past few decades, a plethora of multi-objective algorithms have been proposed, often tested on MOPs possessing two or three objectives. Unfortunately, when tasked with solving MOPs with four or more objectives, referred to as many-objective problems (MaOPs), a large majority of optimizers experience significant performance degradation. The downfall of these optimizers is that simultaneously maintaining a well-spread set of solutions along with appropriate selection pressure to converge becomes difficult as the number of objectives increase. This difficulty is further compounded for large-scale MaOPs, i.e., MaOPs possessing large amounts of decision variables. In this thesis, we explore the challenges of many-objective optimization and propose three new promising algorithms designed to efficiently solve MaOPs. Experimental results demonstrate the proposed optimizers to perform very well, often outperforming state-of-the-art many-objective algorithms.