¾ÅÉ«ÊÓƵ

Evolution by natural selection has created amazingly complex and sophisticated solutions to some very difficult problems - how exactly does it achieve this, and how can we harness this capability for engineering artificial systems and computational problem solving?

The content includes key concepts, tools and approaches in:
- Basic aspects of evolutionary biology,
- Techniques in artificial evolutionary computation,
- Scientific exchange between the two disciplines: e.g. how artificial evolutionary algorithms help us understand the capabilities and limitations of biological evolution, and how current topics in evolutionary biology inspire new solutions to evolvability and scalability in engineering.

This module is intended as an optional module for appropriate part 4 undergraduates and MSc students. Prior completion of specific modules is not a prerequisite for enrolment. This unit introduces basic biological topics to a computer science/numerate audience and assumes no biological background/pre-requisites. However, the module does involve considerable biological as well as computational material, and would suit students with an interest in the theory of evolution and competence in programming.

Evolution by natural selection has created amazingly complex and sophisticated solutions to some very difficult problems - how exactly does it achieve this, and how can we harness this capability for engineering artificial systems and computational problem solving?

The content includes key concepts, tools and approaches in:
- Basic aspects of evolutionary biology,
- Techniques in artificial evolutionary computation,
- Scientific exchange between the two disciplines: e.g. how artificial evolutionary algorithms help us understand the capabilities and limitations of biological evolution, and how current topics in evolutionary biology inspire new solutions to evolvability and scalability in engineering.

This module is intended as an optional module for appropriate part 4 undergraduates and MSc students. Prior completion of specific modules is not a prerequisite for enrolment. This unit introduces basic biological topics to a computer science/numerate audience and assumes no biological background/pre-requisites. However, the module does involve considerable biological as well as computational material, and would suit students with an interest in the theory of evolution and competence in programming.

Evolution by natural selection has created amazingly complex and sophisticated solutions to some very difficult problems - how exactly does it achieve this, and how can we harness this capability for engineering artificial systems and computational problem solving?

The content includes key concepts, tools and approaches in:
- Basic aspects of evolutionary biology,
- Techniques in artificial evolutionary computation,
- Scientific exchange between the two disciplines: e.g. how artificial evolutionary algorithms help us understand the capabilities and limitations of biological evolution, and how current topics in evolutionary biology inspire new solutions to evolvability and scalability in engineering.

This module is intended as an optional module for appropriate part 4 undergraduates and MSc students. Prior completion of specific modules is not a prerequisite for enrolment. This unit introduces basic biological topics to a computer science/numerate audience and assumes no biological background/pre-requisites. However, the module does involve considerable biological as well as computational material, and would suit students with an interest in the theory of evolution and competence in programming.

Evolution by natural selection has created amazingly complex and sophisticated solutions to some very difficult problems - how exactly does it achieve this, and how can we harness this capability for engineering artificial systems and computational problem solving?

The content includes key concepts, tools and approaches in:
- Basic aspects of evolutionary biology,
- Techniques in artificial evolutionary computation,
- Scientific exchange between the two disciplines: e.g. how artificial evolutionary algorithms help us understand the capabilities and limitations of biological evolution, and how current topics in evolutionary biology inspire new solutions to evolvability and scalability in engineering.

This module is intended as an optional module for appropriate part 4 undergraduates and MSc students. Prior completion of specific modules is not a prerequisite for enrolment. This unit introduces basic biological topics to a computer science/numerate audience and assumes no biological background/pre-requisites. However, the module does involve considerable biological as well as computational material, and would suit students with an interest in the theory of evolution and competence in programming.

Evolution by natural selection has created amazingly complex and sophisticated solutions to some very difficult problems - how exactly does it achieve this, and how can we harness this capability for engineering artificial systems and computational problem solving?

The content includes key concepts, tools and approaches in:
- Basic aspects of evolutionary biology,
- Techniques in artificial evolutionary computation,
- Scientific exchange between the two disciplines: e.g. how artificial evolutionary algorithms help us understand the capabilities and limitations of biological evolution, and how current topics in evolutionary biology inspire new solutions to evolvability and scalability in engineering.

This module is intended as an optional module for appropriate part 4 undergraduates and MSc students. Prior completion of specific modules is not a prerequisite for enrolment. This unit introduces basic biological topics to a computer science/numerate audience and assumes no biological background/pre-requisites. However, the module does involve considerable biological as well as computational material, and would suit students with an interest in the theory of evolution and competence in programming.

Evolution by natural selection has created amazingly complex and sophisticated solutions to some very difficult problems - how exactly does it achieve this, and how can we harness this capability for engineering artificial systems and computational problem solving?

The content includes key concepts, tools and approaches in:
- Basic aspects of evolutionary biology,
- Techniques in artificial evolutionary computation,
- Scientific exchange between the two disciplines: e.g. how artificial evolutionary algorithms help us understand the capabilities and limitations of biological evolution, and how current topics in evolutionary biology inspire new solutions to evolvability and scalability in engineering.

This module is intended as an optional module for appropriate part 4 undergraduates and MSc students. Prior completion of specific modules is not a prerequisite for enrolment. This unit introduces basic biological topics to a computer science/numerate audience and assumes no biological background/pre-requisites. However, the module does involve considerable biological as well as computational material, and would suit students with an interest in the theory of evolution and competence in programming.

There has been growing interest in the past few years in how cities and regions respond and adapt to rapid, and often turbulent, economic change, and why some cities and regions appear much more successful than others in coping with and taking advantage of such change. The aim of this module is
to examine a new evolutionary economic geography which explores how ideas and concepts from a number of sciences concerned with the evolution of complex systems can be used to explain regional change and adaptability. The course considers how the economic structures and activities of cities and regions economies are shaped by rapidly changing global market conditions and competition, technological change, and shifts in public policy and modes of political–economic governance. How relevant are the ideas of emergence, self-organisation, path creation, adaptive cycles, robustness and resilience to the study of city and regional economies? Using a range of examples from different types of economy, it examines how processes of creative destruction produce the rise of new industries and the decline of others. It compares different types of regional innovation systems and their knowledge networks. It outlines some of the recent dynamics of global production networks, and it reviews the consequences of these processes for different economic regions. It considers some of the ways in which economic processes are set within variegated and differentiated regulatory contexts and how these result in varied experiences.

There has been growing interest in the past few years in how cities and regions respond and adapt to rapid, and often turbulent, economic change, and why some cities and regions appear much more successful than others in coping with and taking advantage of such change. The aim of this module is
to examine a new evolutionary economic geography which explores how ideas and concepts from a number of sciences concerned with the evolution of complex systems can be used to explain regional change and adaptability. The course considers how the economic structures and activities of cities and regions economies are shaped by rapidly changing global market conditions and competition, technological change, and shifts in public policy and modes of political–economic governance. How relevant are the ideas of emergence, self-organisation, path creation, adaptive cycles, robustness and resilience to the study of city and regional economies? Using a range of examples from different types of economy, it examines how processes of creative destruction produce the rise of new industries and the decline of others. It compares different types of regional innovation systems and their knowledge networks. It outlines some of the recent dynamics of global production networks, and it reviews the consequences of these processes for different economic regions. It considers some of the ways in which economic processes are set within variegated and differentiated regulatory contexts and how these result in varied experiences.

There has been growing interest in the past few years in how cities and regions respond and adapt to rapid, and often turbulent, economic change, and why some cities and regions appear much more successful than others in coping with and taking advantage of such change. The aim of this module is
to examine a new evolutionary economic geography which explores how ideas and concepts from a number of sciences concerned with the evolution of complex systems can be used to explain regional change and adaptability. The course considers how the economic structures and activities of cities and regions economies are shaped by rapidly changing global market conditions and competition, technological change, and shifts in public policy and modes of political–economic governance. How relevant are the ideas of emergence, self-organisation, path creation, adaptive cycles, robustness and resilience to the study of city and regional economies? Using a range of examples from different types of economy, it examines how processes of creative destruction produce the rise of new industries and the decline of others. It compares different types of regional innovation systems and their knowledge networks. It outlines some of the recent dynamics of global production networks, and it reviews the consequences of these processes for different economic regions. It considers some of the ways in which economic processes are set within variegated and differentiated regulatory contexts and how these result in varied experiences.

There has been growing interest in the past few years in how cities and regions respond and adapt to rapid, and often turbulent, economic change, and why some cities and regions appear much more successful than others in coping with and taking advantage of such change. The aim of this module is
to examine a new evolutionary economic geography which explores how ideas and concepts from a number of sciences concerned with the evolution of complex systems can be used to explain regional change and adaptability. The course considers how the economic structures and activities of cities and regions economies are shaped by rapidly changing global market conditions and competition, technological change, and shifts in public policy and modes of political–economic governance. How relevant are the ideas of emergence, self-organisation, path creation, adaptive cycles, robustness and resilience to the study of city and regional economies? Using a range of examples from different types of economy, it examines how processes of creative destruction produce the rise of new industries and the decline of others. It compares different types of regional innovation systems and their knowledge networks. It outlines some of the recent dynamics of global production networks, and it reviews the consequences of these processes for different economic regions. It considers some of the ways in which economic processes are set within variegated and differentiated regulatory contexts and how these result in varied experiences.