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The Lie: Evolution


Ants Compilation

In 2022, due to the fact of the large number of articles that bug evolutionism, I began putting multiple articles into loose categories. This one is ANTS.


1. Ant Behavior Informs Computer Search Algorithms

2. Ants Demonstrate Characteristics of Engineered Adaptability

3. Scientists Discover the 'Anternet'

4. 'Talking' Ants Are Evidence for Creation

5. To Study Human Brains, Evolutionists Studied...Ants

6. To Regulate Foraging, Harvester Ants Use a (Designed) Feedback Control Algorithm

Ant Behavior Informs Computer Search Algorithms

By Jeffrey P. Tomkins, June 22, 2020

The social behavior of ants continues to amaze scientists with its complexity and efficiency of organization and design. In a new study, scientists have shown how ant communities foraging for food and other resources embody a complex system of behavior and path marking that creates an optimized search with minimal waste of energy and time.1 The information gleaned from this study was then applied to the design of computer search algorithms.

Social animals, like ants, that function in big groups encounter the challenge of exploring their surroundings to look for food, water, resources, and even places to make a new nest. The problem with performing these search operations with such a large group of individuals, like an ant colony, is that a huge amount of time and effort would be wasted if the ants repeatedly kept exploring the same empty areas. Therefore, a system of keeping track of what the search teams have already explored to avoid revisiting unprofitable areas is a distinct advantage. In fact, this type of efficiency-driven behavior has been observed in insects and other creatures, but little is known about the details.

In this new research, an interdisciplinary group from the University of Bristol's Faculties of Engineering and Life Sciences, hypothesized that the efficient foraging activities of the rock ant utilized some form of chemical marking and communication. This would prevent the ants from exploring the same areas multiple times. The lead investigator Edmund Hunt said, "This would be a reversal of the Hansel and Gretel story—instead of following each other's trails, they would avoid them in order to explore collectively.”2

To test their theory, an experiment was set up where ants were allowed to explore an empty arena one by one. Under one condition, the researchers cleaned the space between each ant’s search so no chemical marker of the search path would be left. In a second condition, the search spaces between ants were left unaltered. The ants operating under the second condition with no cleaning made a considerably more efficient exploration of the area without repeating the efforts of previous ants, and thus covered more space.

In human-designed search algorithms, it is important to avoid searching space and data in a redundant fashion which, under very simple conditions, is fairly easy to program. However, many real-world science and engineering problems can be highly complex, and it is difficult to develop a neat mathematical solution. Thus, the computerized sampling of diverse, complex, and large data sets often involves methods to only obtain a good approximation for a search based on statistical probabilities. The general goal is to avoid sampling too much data from unimportant or low probability portions of the overall data landscape.

By observing the search process of ants, the researchers were inspired to improve their algorithms by adopting an ant-inspired system to speed up and optimize the process. Edmund Hunt said,

We predicted that we could simulate the approach adopted by the ants in the mathematical sampling problem, by leaving behind a 'negative trail' of where has already been sampled. We found that our ant-inspired sampling method was more efficient (faster) than a standard method which does not leave a memory of where has already been sampled.2

In fact, the ant behavior was so informative that Hunt also went on to say,

Our ant-inspired sampling method may be useful in many domains, such as computational biology, for speeding up the analysis of complex problems. By describing the ants' collective behavior in informational terms, it also allows us to quantify how helpful are different aspects of their behavior to their success. For example, how much better do they perform when their pheromones are not cleaned away. 2

This research immediately calls to mind a saying of Solomon in the book of Proverbs which states, “Go to the ant, you sluggard! Consider her ways and be wise, which, having no captain, overseer or ruler, provides her supplies in the summer, and gathers her food in the harvest.”3 Truly, the evidence of God’s design shines forth once again in His amazing creation. Even the seemingly lowly ant continues to amaze and reveal the creative genius of our mighty Creator.


1. Edmund R. Hunt et al. 2020. The Bayesian superorganism: externalized memories facilitate distributed sampling. Journal of the Royal Society Interface. DOI: 10.1098/rsif.2019.0848.

2. Staff Writer. An ant-inspired approach to mathematical sampling. PhysOrg. Posted on June 19, 2020, accessed June 19, 2020.

3. Proverbs 6:6-8.

Ants Demonstrate Characteristics of Engineered Adaptability

By Randy J. Guliuzza, P.E., M.D. March 24, 2020

Darwin’s theory of evolution makes several predictions about adaptation. But recent genetic findings raise questions about the accuracy of evolutionary theory, since the findings point toward different types of engineered adaptability. The latest challenges to Darwin’s theory, published in March 2020, come from the fascinating insects known as turtle ants.

Scott Powell from George Washington University led a team conducting research on turtle ants (genus Cephalotes).1 All ants in a colony that descend from the queen are expected to be genetically identical. Yet, many ants develop into very different-looking types such as workers, drones, and queens (called castes) that carry out different roles for the good of the colony. Turtle ants establish nests in trees that occupy the abandoned tunnels of wood boring beetles. The caste of turtle ants known as soldiers defends the entrances to their nest by using their heads as living barricades. As can be seen in Figure 1, different species of turtle ants develop remarkably different armor for their heads which give their soldiers’ heads distinctive shapes. Different head shapes, used individually or in groups, fit different sized holes made by different wood boring beetles. For example, in the turtle ant species with the dish-shaped head, the head of a single ant can completely cover the entrance to the nest.

When considering how these differently shaped ants heads originated in over 100 species of turtle ants, at least three interesting questions arise: Is there evidence that these different species descended from a common ancestor? Do the differences in the shape of their heads affect how turtle ants colonize new territories? Is the development of diverse head shapes consistent with classical evolutionary theory?

For Darwin, if adaptation was going to be consistent with his theory, then it needed to conform to three characteristics: copious variation in traits, small in extent (i.e., gradual), and undirected.2 In addition, classical evolutionary theory predicts that the trajectory of change is one-way, which means that it is irreversible. The tenet of irreversible evolution is known as “Dollo’s Law.”3

The title of Powell’s article, “Trait Evolution is reversible, repeatable, and decoupled in the soldier caste of turtle ants,”1 should be quite a shock to evolutionary theorists. Genetic analysis of the diverse species did indicate they descended from a common ancestor, but not as would be characterized by evolutionary theory. The researchers found “repeated evolutionary gains and losses of the four morphotypes were reconstructed consistently across multiple analyses,” and “multiple model-fitting approaches suggested that soldier head size evolution is best explained by a process of divergent pulses of change.”

Can a better model replace classical evolutionary—a theory that is consistent with the abundant discoveries in recent biological research?

In 2017, ICR began to develop an engineering-based, organism-focused model of adaptation known as Continuous Environmental Tracking (CET).4 It postulates that what creatures actually appear to be doing is tracking changes in external conditions utilizing many internal mechanisms. These innate systems use the same well-matched elements underlying the self-adjustable property of human-engineered tracking systems. These are (1) input “sensors” to gather data on external conditions, (2) internal programming specifying reference values and “logic segments” which compares input data to a reference and selects a suitable response, and (3) output “actuators” to execute responses.

Very similar to Powell’s conclusion, but several years earlier, ICR’s article went on to say,

These recently outlined internal mechanisms [citing biological research] have some surprising characteristics. These innate mechanisms yield results that are regularly described as “regulated,” “rapid,” very often “repeatable,” and, surprisingly at times, even “reversible”—words that fit the outcomes of engineered systems.”

Our research concluded,

When researchers see recurrent, similar categories of change that are described as being regulated, rapid, and repeatable, they should recognize them as corresponding to distinctive expectations of design…this would imply that both internal form and adaptability are governed by internal systems. Thus, the total validity of Darwin’s externalistic theory itself, not merely its sufficiency, is challenged by the reality of intelligent design.

Powell’s research team didn’t describe how rapid the change in head shape for turtle ants could occur. But, remarkably, they did find a broad range in head sizes (within each type of head shape) that seem to match the size of hole left by different wood boring beetles. They also found that head size for soldier ants is expressed independently of, or “decoupled” from, queen ant morphology. This means that “species with similar queen phenotypes can evolve [i.e., “adapt”] substantially different soldier head sizes, and thus fill differentiated nesting niches.”5 Thus, we see that over time the variable trait for head size facilitates diverse populations of turtle ants fit and fill the unique sizes and shapes of bore holes. From an engineering perspective, these populations could be seen as closely tracking, through time, the variable environmental conditions of bore hole size.

Powell’s thought-provoking conclusion is that, “These findings reshape our understanding of caste evolution, rejecting a stable, directional process in favor of a dynamic process of adaptive fitting between phenotype and environment.” His findings fit the CET model of adaptation so well that they constitute good evidence to justify not simply reshaping our understanding of evolution but replacing Darwin’s model with CET.

More work needs to be done to find the innate mechanism that enable changes in head shape and size and to nail down how fast it can happen. Appeals to mystical “selection pressures” will only sidetrack accurate explanations. After all, how many soldier ants would have to die through a trial-and-error process before one is discovered with the right head size to fit a new nest’s borehole entrance variable size? And even then, that soldier ant and won’t pass on its traits to other soldier ants, as only the queen’s genes are inherited by the colony. What is more likely to be discovered is a highly regulated internal mechanism that enables rapid, repeatable, and reversible adjustments of traits to conditions…a mechanism that reveals the incomparable engineering genius of the Lord Jesus Christ (Romans 1:20).


1. Powell, S. et al. 2020. Trait evolution is reversible, repeatable, and decoupled in the soldier caste of turtle ants. Published at on March 9, 2020, accessed March 13, 2020.

2. Gould, S. J. 2002. The Structure of Evolutionary Theory. Cambridge, MA: Harvard University Press, 141-145. See also, Guliuzza, R. J. 2018. Engineered Adaptability: Adaptive Changes Are Purposeful, Not Random. Acts & Facts. 47 (6): 17-19.

3. Guliuzza, R. J. 2016. Major Evolutionary Blunders: Breaking Dollo’s Law. Acts & Facts. 45 (7): 16-18.

4. Guliuzza, R. J. 2017. Engineered Adaptability: Arriving at a Design-Based Framework for Adaptability. Acts & Facts. 46 (8): 17-19. See also, Guliuzza, R. J. and P. B. Gaskill. 2018. Continuous environmental tracking: An engineering framework to understand adaptation and diversification. In Proceedings of the Eighth International Conference on Creationism, ed. J.H. Whitmore. Pittsburgh, Pennsylvania: Creation Science Fellowship, 158–184.

5. Powell, Trait evolution is reversible, emphasis added.

Scientists Discover the 'Anternet'

By Brian Thomas, September 14, 2012

Ants seem to cover the globe. The success of any ant colony depends on the inherent capabilities of its many working members. One of the capabilities of harvester ants relies on an algorithm that governs their foraging frequency. It happens to parallel an internet data management algorithm.

Harvester ants forage for seeds, with each ant carrying one seed at a time back to its colony. If too many ants are out harvesting seeds, ant resources are wasted, and the colony could receive a glut of produce that would create traffic jams and food overstocking issues. That could spell ant disaster. On the other hand, the whole colony starves if they fail to bring in enough seed for food. How do ants keep such tight regulation on this critical task? Researchers were also curious about how harvester ants accomplish this with no central administration.

The answer is that each ant carries its own understanding. By experimenting with harvester ants, biologists determined that when ants return with food more slowly, then more of them know to leave the colony to go foraging. Also, when ants return in large numbers, quickly filling the coffers, fewer go foraging.

Now, biologists and engineers from Stanford partnered together to find that an algorithm commonly used to regulate web traffic and other data streams closely matches that which harvester ants use to regulate harvesting traffic. They published their results in the online journal PLoS Computational Biology.1

They figured that the algorithm in ant brains uses four variables. One describes the rate of outgoing foragers, another describes the amount that the rate increases with each returning ant, and another describes the amount that the rate decreases with each outgoing ant. The study authors related these parameters using two formulae:

an = max(an-1-qDn-1+cAn-d,a), a0 = 0

Dn~Poisson (an)

Who knew ants were so wise?

And of course, harvester ants know so much more. For example, they know what temperature range is healthy for them to forage. They go deep underground during very hot times in the deserts of the Western United States where many of them live. On sunny days, harvester ants usually cease foraging by 11a.m. and restart when temperatures drop to 118-125°F.2 This means that ants have internal thermometers and that those thermometers communicate with their decision centers.

Clearly, ants do not go to school. Thus, they must have been given their wisdom from a source outside themselves.3


Prabhakar, B., K. N. Dektar, and D. M. Gordon. The Regulation of Ant Colony Foraging Activity without Spatial Information. PLoS Computational Biology. 8 (8): e1002670.

Moody, J.V. and D.E. Foster. 1979. Notes on the Bionomics and Nest Structure of Pogonomyrmex maricopa(Hymenoptera: Formicidae). In Genoways, H.H. and R.J. Baker, eds. 1979. National Park Service Proceedings and Transactions Series. Biological Investigations in the Guadalupe Mountains National Park, Texas. 4: 115-121.

Thomas, B. Ant Algorithms Argue against Evolutionary Origins. ICR News. Posted on, February 17, 2009, accessed September 5, 2012.

'Talking' Ants Are Evidence for Creation

By Jeffrey P. Tomkins, February 22, 2013

New surprises revealing complex bio-engineering keep emerging as evolutionary scientists continue to unwittingly obey the biblical command to "observe the ant"(Proverbs 6:6; 30:25). The latest bio-engineering discovery is that a key component of ant colony survival is based on sound (acoustic) communication systems.1

One of the long-standing paradigms of animal communication is the use of airborne chemical messages called pheromones. Ants use pheromones to leave chemical trails that can be followed by other members and to also identify which nest an ant is from, along with its social status in the colony. Now, scientists can add yet another layer of complexity and communication in ant colonies based on acoustics.

Scientists have been studying a type of ant commonly found in Europe. This ant has a specialized appendage on its abdomen that it strokes with its hind legs to create sound signals. Other ants can detect and process these signals, resulting in various complex social responses that are key to survival of the colony. Several years ago, researchers found that, in adult ants, these signals can act like an emergency beacon when an ant is threatened by a predator.2

If the discovery of this complex signaling in adult ants was not enough of a surprise, scientists have now determined that developing larvae back in the nest also use this technique, which is important for the ant colony's survival. Everything in an ant colony is performed in an orderly manner.

When an ant nest is disturbed and threatened, the worker ants immediately go about rescuing the nest. First, they grab and remove the mature larvae and then the immature larvae and pupae. As it turns out, the mature larvae use acoustic communication via their early maturing acoustic appendage, which the younger larvae and pupae lack, to signal their social status to the worker ants, enabling them to be extricated first (see image below). In the event of settling a new colony, the mature larvae would hatch first and thus be more valuable assets than the younger larvae, which require more resources.

It is also noteworthy that the acoustic signals are not performed in isolation, but co-processed along with other pheromone sensory signals using complex internal bioprocessing systems. Several other news articles from the Institute for Creation Research have discussed the complexity of ant colonies and their management through highly engineered bioprocessing systems.3,4,5

The combination of various sensory communication and processing systems are a clear example of an all-or-nothing suite of features referred to as irreducible complexity. All the ants would die in one generation if you remove any one of these features: 1) early maturing abdominal acoustic appendage, 2) instinct to "strum"it, 3) sensors in adults to detect it, 4) ant brains to interpret the sounds, and 5) the instinct to protect the mature larvae.

These new discoveries are amazing testimonies to the intelligence of the powerful Creator who engineered these remarkable living systems that utterly defy evolutionary dogma.


Casacci, L. P. et al. Ant Pupae Employ Acoustics to Communicate Social Status in Their Colony's Hierarchy. Current Biology. Published online before print, February 7, 2013.

Barbero, F. et al. Myrmica Ants and Their Butterfly Parasites with Special Focus on the Acoustic Communication. Psyche. 2012 (2012).

Thomas, B. Scientists Discover the 'Anternet.' Creation Science Update. Posted on September 14, 2012, accessed February 11, 2013.

Thomas, B. Ant Algorithms Argue Against Evolutionary Origins. Creation Science Update. Posted on February 17, 2009, accessed February 11, 2013.

Tomkins, J. Communal Nutrition in Ants: Strong Evidence for Creation. Creation Science Update. Posted on July 8, 2009, accessed February 11, 2013.

To Study Human Brains, Evolutionists Studied...Ants

By Frank Sherwin, November 08, 2021

The book of Proverbs states, “Go to the ant, you sluggard! Consider her ways and be wise” (6:6). Evolutionists went to the ant, not to learn of her God-given ability to gather and store provisions, but to vainly attempt to determine human brain evolution. Human brain size has decreased since 3,000 years ago and is a mystery to anthropologists.

To disentangle this mystery, a team of researchers from different academic fields set out to study the historical patterns of human brain evolution, comparing their findings with what is known in ant societies to offer broad insights.1

Ants are small, industrious insects at the base of the terrestrial ecosystem. God created them with sophisticated social systems, helping them to efficiently interact with each other and the environment.2

Because ants—like people—have been created with complex societal interactions and because evolutionists have no recourse but to embrace evolutionism, they must make the best of a terrible theory and somehow make a tenuous connection between human and ant brains and their behavior.

Ants provide a wide range of social systems to generate and test hypotheses concerning brain size enlargement or reduction and aid in interpreting patterns of brain evolution identified in humans. Although humans and ants represent very different routes in social and cognitive evolution, the insights ants offer can broadly inform us of the selective forces that influence brain size.3

Are the authors serious? Evidently so. But the human (and ant) brain did not evolve.

It is currently unknown when and in what form the central nervous system (CNS) in Bilateria first appeared, and how it further evolved in the different bilaterian phyla.4

A decade later the picture has not changed, “When and how the animal nervous system arose has remained murky….”5

The difference between arthropods (ants) and mammals (humans) are, of course, legion. But evolutionary theory maintains they must have a common ancestor. How do evolutionists start by making a connection between ants and people? By implication,

A natural starting point in reconstructing the evolutionary pathways of neural and behavioral complexity is from the nature of the common ancestor of the major bilaterian radiations. While the animal is doubtless extinct, its nature can be inferred.6

Not only has the common ancestor between arthropods and mammals remain unknown, but ants have always been ants.7 One of the earliest known ants comes from the genus Gerontoformica. It is from Charentese amber in France dated by evolutionists at over 100 million years old—and it is 100% ant.

Evolutionary naturalists can only appeal to ethereal pressures and forces of selection, unknown common ancestors, and inferences to make their case for brain evolution between ants and people. This is not science.

A ScienceAlert article admits, “The authors acknowledge their hypothesis is based on a ‘theory of theories’ that probably can't explain all the size changes in our brains throughout our evolutionary history.”8

Creationists couldn’t agree more.


1. Frontiers. When and why did human brains decrease in size 3,000 years ago? Ants may hold clues. Posted on October 22, 2021, accessed October 28, 2021.

2. Tomkins, J. ‘Talking’ Ants Are Evidence for Creation. Creation Science Update. Posted on February 22, 2013, accessed November 2, 2021; Tomkins, J. Ant Behavior Informs Computer Search Algorithms. Creation Science Update. Posted on June 22, 2020, accessed November 2, 2021.

3. DeSilva, J. et al. 2021. When and Why Did Human Brains Decrease in Size? A New Change-Point Analysis and Insights from Brain Evolution in Ants. Frontiers in Ecology & Evolution. 2021 (9): 712.

4. Arendt, D. et al. 2009. The evolution of nervous system centralization. Philosophical Transactions B of the Royal Society. 353 (1496): 1523-1528.

5. Pennisi, E. 2019. Did neurons arise from an early secretory cell. Science. 363 (6424).

6. Gillette & Brown. 2015. The Sea Slug. Integrative & Comparative Biology. 55 (1058).

7. Thomas, B. 120-Million-Year-Old Ants Alive and Well? Creation Science Update. Posted on Sept 24, 2008, accessed November 2, 2021.

8. Cassella, C. 2021. Ants Could Help Explain Why Our Brains Mysteriously Shrank Thousands of Years Ago. Posted on October 22, 2021, accessed October 29, 2021.

To Regulate Foraging, Harvester Ants Use a (Designed) Feedback Control Algorithm
Eric Cassell April 7, 2022

A recent study in the Journal of the Royal Society Interface reports on “A feedback control principle common to several biological and engineered systems.” The researchers, Jonathan Y. Suen and Saket Navlakha, show how harvester ants (Pogonomyrmex barbatus) use a feedback control algorithm to regulate foraging behavior. As Science Daily notes, the study determined that, “Ants and other natural systems use optimization algorithms similar to those used by engineered systems, including the Internet.”

The ants forage for seeds that are widely scattered and usually do not occur in concentrated patches. Foragers usually continue their search until they find a seed. The return rate of foragers corresponds to the availability of seeds: the more food is available, the less time foragers spend searching. When the ants successfully find food, they return to the nest in approximately one third of the search time compared to ants unable to find food. There are several aspects of this behavior that point to intelligent design.

Feedback Control

First, it is based on the general engineering concept of a feedback control system. Such systems use the output of a system to make adjustments to a control mechanism and maintain a desired setting. A common example is the temperature control of heating and air conditioning systems. An analogy in biology is homeostasis, which uses negative feedback, and is designed to maintain a constant body temperature.

Mathematical Algorithm

A second aspect of design is the algorithm used to implement the specific control mechanism. Suen and Navlaka describe the system as “multiplicative-increase multiplicative-decrease” (MIMD). The MIMD closed loop system is a hybrid combination of positive and negative feedback. Receiving positive feedback results in multiplying the response, while negative feedback results in reducing the response by a constant value. The purpose relates to the challenge of optimizing ant foraging. As the paper explains:

If foraging rates exceed the rate at which food becomes available, then many ants would return “empty-handed,” resulting in little or no net gain in colony resources. If foraging rates are lower than the food availability rate, then seeds would be left in the environment uncollected, meaning the seeds would either be lost to other colonies or be removed by wind and rain.

The authors found that positive feedback systems are “used to achieve multiple goals, including efficient allocation of available resources, the fair or competitive splitting of those resources, minimization of response latency, and the ability to detect feedback failures.” However, positive control feedback systems are susceptible to instability (think of the annoying screech when there is feedback into microphones in a sound system). Therefore, a challenge for MIMD systems is to minimize instability.

In this application, when foraging times are short, the feedback is positive, resulting in a faster increase in the number of foragers. When foraging times are longer, the feedback is negative, resulting in a reduction in the number of foragers. A mathematical model of the behavior has confirmed that the control algorithm is largely optimized. (See Prabhakar et al., “The Regulation of Ant Colony Foraging Activity without Spatial Information,” PLOS Computational Biology, 2012.) As I describe in my recent book, Animal Algorithms, the harvester ant algorithm is just one example of behavior algorithms that ants and other social insects employ.

Suen and Navlakha point out that the mechanism is similar to that employed to regulate traffic on the Internet. In the latter context, there are billions of “agents” continuously transmitting data. Algorithms are employed to control and optimize traffic flow. The challenge for Internet operations is to maximize capacity and allow for relatively equal access for users. Obviously, Internet network control is designed by intelligent engineers. In contrast, the harvester ant behavior is carried out by individuals without any central control mechanism.

Physical Sensors

A third feature indicating design is the physical mechanism used by the ants to determine how long returning foragers have been out. When ants forage for food, molecules called cuticular hydrocarbons change based on the amount of time spent foraging. This is due to the difference in temperature and humidity outside of the nest. As the ants return to the entrance of the nest, there are interactions between the returning and the outgoing ants via their antennae. These interactions enable detection of the hydrocarbons, which provide a mechanism to enable outgoing ants to determine the amount of time that returning ants spent foraging.

These three elements of harvester ant behavior (feedback control, mathematical algorithm, and physical sensors) present a severe challenge for the evolutionary paradigm. From a Darwinian perspective, they must have arisen through a combination of random mutations and natural selection. A much more plausible explanation is that they are evidence of intelligent design.