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6 course outlines found

1

ENGSCI 760

: Algorithms for Optimisation
2024 Semester One (1243)
Meta-heuristics and local search techniques such as Genetic Algorithms, Simulated Annealing, Tabu Search and Ant Colony Optimisation for practical optimisation. Introduction to optimisation under uncertainty, including discrete event simulation, decision analysis, Markov chains and Markov decision processes and dynamic programming.
Subject: Engineering Science
Prerequisite: 15 points from COMPSCI 101, ENGGEN 131, MATHS 162, 199, and 15 points from COMPSCI 120, ENGSCI 111, STATS 125
2

ENGSCI 760

: Algorithms for Optimisation
2023 Semester One (1233)
Meta-heuristics and local search techniques such as Genetic Algorithms, Simulated Annealing, Tabu Search and Ant Colony Optimisation for practical optimisation. Introduction to optimisation under uncertainty, including discrete event simulation, decision analysis, Markov chains and Markov decision processes and dynamic programming.
Subject: Engineering Science
Prerequisite: COMPSCI 101 or ENGGEN 131
3

ENGSCI 760

: Algorithms for Optimisation
2022 Semester One (1223)
Meta-heuristics and local search techniques such as Genetic Algorithms, Simulated Annealing, Tabu Search and Ant Colony Optimisation for practical optimisation. Introduction to optimisation under uncertainty, including discrete event simulation, decision analysis, Markov chains and Markov decision processes and dynamic programming.
Subject: Engineering Science
Prerequisite: COMPSCI 101 or ENGGEN 131
4

ENGSCI 760

: Algorithms for Optimisation
2021 Semester One (1213)
Meta-heuristics and local search techniques such as Genetic Algorithms, Simulated Annealing, Tabu Search and Ant Colony Optimisation for practical optimisation. Introduction to optimisation under uncertainty, including discrete event simulation, decision analysis, Markov chains and Markov decision processes and dynamic programming.
Subject: Engineering Science
Prerequisite: COMPSCI 101 or ENGGEN 131
5

ENGSCI 760

: Algorithms for Optimisation
2020 Semester One (1203)
Meta-heuristics and local search techniques such as Genetic Algorithms, Simulated Annealing, Tabu Search and Ant Colony Optimisation for practical optimisation. Introduction to optimisation under uncertainty, including discrete event simulation, decision analysis, Markov chains and Markov decision processes and dynamic programming.
Subject: Engineering Science
Prerequisite: COMPSCI 101 or ENGGEN 131
6

ENGSCI 760

: Algorithms for Optimisation
2025 Semester One (1253)
Meta-heuristics and local search techniques such as Genetic Algorithms, Simulated Annealing, Tabu Search and Ant Colony Optimisation for practical optimisation. Introduction to optimisation under uncertainty, including discrete event simulation, decision analysis, Markov chains and Markov decision processes and dynamic programming.
Subject: Engineering Science
Prerequisite: 15 points from COMPSCI 101, ENGGEN 131, MATHS 162, 199, and 15 points from COMPSCI 120, ENGSCI 111, STATS 125

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