Clipping advertising the auction to be held at 28 College Street in Toronto, Ontario

Clipping advertising the auction to be held at 28 College Street in Toronto, Ontario, April 1939.

Catalogue of items from Devaux [DeVeaux] Hall to be sold in Toronto at auction

Catalogue of items from Devaux [DeVeaux] Hall to be sold in Toronto at auction on April 24-26, 1939. The list of items included items such as lamps, curtain rods, furniture, original art, vases, kitchen items and books. The auction was held in 6 sessions over a 3 day period. There were 855 items listed in the 45 page catalogue. The auction was put on by Ward-Price Auctioneers of Toronto, Apr. 1939.

Indenture between James Reid and James Henderson of Toronto

Indenture between James Reid and James Henderson of Toronto for 200 acres on Lot no. 10 in the 19th Concession in the Township of Tiny in Simcoe County. This document is dated Aug. 4, 1854. It is attached to another indenture between James Henderson of Toronto and The Honourable Walter H. Dickson for the aforesaid land. The second document is torn. This does not affect the text, Jan. 1, 1857.

Receipt from Watkins and Harris, Wholesale and Retail Ironmongers, Toronto, Ontario for iron

Receipt from Watkins and Harris, Wholesale and Retail Ironmongers, Toronto, Ontario for iron, May 11, 1838.

Letter to Colonel Henry Nelles from Colonel William Bullock, Adjt. General of Militia of Toronto

Letter to Colonel Henry Nelles from Colonel William Bullock, Adjt. General of Militia of Toronto stating that His Excellency the Lieutenant Governor is pleased to accept the resignation of Lieutenant Thomas Waddell. This letter is attached to a letter to Colonel Richard Bullock from Henry Nelles reporting on the weekly state of the 4th Lincoln Militia. There is also a copy of the half yearly return of recommendation and appointments of the 4th Regiment of the Lincoln Militia (2 pages in total, handwritten), Dec. 13, 1838.

Blueprint of the plan of the City of Toronto

Blueprint of the plan of the City of Toronto (85 cm. x 140 cm.), 1857.

Blueprint (inverted image) of the plan of the City of Toronto

Blueprint (inverted image) of the plan of the City of Toronto (85 cm. x 140 cm.), 1857.

The Constitution, Toronto, Upper Canada, December 21, 1836

The Constitution, Toronto, Upper Canada, December 21, 1836.

The Constitution, Toronto, Upper Canada, March 1, 1837

The Constitution, Toronto, Upper Canada, March 1, 1837.

The Constitution, Toronto, Upper Canada, April 12, 1837

The Constitution, Toronto, Upper Canada, April 12, 1837.

The Constitution, Toronto, Upper Canada (2 copies), September 6, 1837

The Constitution, Toronto, Upper Canada (2 copies), September 6, 1837.

Mackenzie’s Toronto Weekly Message, July 16, 1859

Mackenzie’s Toronto Weekly Message, July 16, 1859.

Mackenzie’s Toronto Weekly Message, July 30, 1859

Mackenzie’s Toronto Weekly Message, July 30, 1859.

The Weekly Leader, Toronto, Ontario, June 16, 1866

The Weekly Leader, Toronto, Ontario, June 16, 1866.

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.